Science 4-Hire: Recent Episodes

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“You buy the hiring platform, and then you don’t have these AI workflows enabled... here you are three, four years later. You’re still not able to do that.”

— Nicole Mundy

Episode Overview

In this episode I’m joined by Nicole Mundy, Senior Research Analyst at Talentech Labs — a research and advisory firm that helps its enterprise clients evaluate and procure hiring technology systems.

Nicole brings a valuable perspective to the table because she sees the dynamic between vendor and buyer up close and personal. When combined with my experience in this same realm from the science side- our discussion shines light on the reality of what is happening in AI tech adoption for TA.

Topics Discussed & Key Insights

1. Companies are buying AI hiring tools at scale — and then leaving them switched off.

Among the world’s top enterprise organizations, Nicole estimates a surprisingly small amount are actually using AI to automatically assess active applicants.

Why?

Approvals never come - Companies buy the platform intending to enable the AI, “once the right approvals are in place- but years often pass without any change.

Pilots underperform - Big companies test these tools and often conclude they can’t really use them, or they just didn’t work.

Lack of solid ROI evidence - Despite the best intentions- most companies do not do the follow up work needed to demonstrate the impact of these tools on the bottom line.

Legal ambiguity freezes decisions - With regulations constantly in flux, risk management often takes priority over business needs.

2. Validation is misunderstood and absent.

What vendors without I/O science guidance call validation isn’t what legal compliance actually requires.

• Vendors are quick to speak about the validity of their solution and talk endlessly about validating their AI models — running statistical checks that the model predicts consistently and de-biasing its outputs across groups. This is purely empirical work.

• But that’s IT-style validation. It confirms the system runs as built; it says nothing about whether the tool is fair or job-related

• Validation for legal compliance, and sound science, demands a blend of rational and empirical work to document the job-relatedness of any tool used to make employment decisions

3. “Skills” are everywhere, and nowhere.

Skills-based hiring is the headline everyone wants. The problem is what counts as a skill.

• Most platforms apply the “skill” label with no objective framework to define it. A skill ends up being little more than a tag like “Excel” or “communication”

• The definitions behind these labels are usually poorly organized and loosely constructed.

• There is no connection between the skills a platform claims to measure and any outcome on the job. Without that link, there is nothing for a buyer to trust or defend.

• The companies doing skills-based hiring well are not buying one vendor and flipping a switch. They run multi-year programs: define the skills objectively, inventory what the organization has against what it needs, curate tools carefully on the front end, and collect assessment data at multiple points.

4. Cheating is a zero-sum game, so let’s change the rules

AI-assisted candidate fraud, from AI completing assessments to coaching candidates through interviews, is driving enterprises toward more dynamic evaluation that is harder to game. But chasing detection is largely a losing battle.

• Trying to catch and block AI use is whack-a-mole. It’s a zero-sum game, and it’s frustrating, because the tools keep getting better and the detection never really gets ahead

• There’s a more useful way to think about it. People are going to use AI on the job, so why not let them use it in the application process in a controlled way? The question stops being “did they use AI” and becomes “how well do they use it”

• Most large organizations are still just trying to get visibility on how much cheating is happening and what it looks like, which says how early everyone is on this.

• Across the approaches Nicole sees, one thing tends to hold up whether a process is locked down or fully AI-assisted: competency-based follow-up questions that make candidates explain their own reasoning in their own words.

Final Takeaway

Enterprise isn’t slow on AI in hiring because it doesn’t understand the technology. It’s slow because the tools are bought on vendor claims that were never reviewed against real science, and the danger only becomes clear once the tool is in play. The companies getting it right aren’t chasing tools. They’re building programs, science first. Held to that bar, many of the tools on the market today wouldn’t survive the review, and the ones that would wouldn’t be sitting switched off.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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In this episode I’m joined by Robert Newry, Founder & CEO of the assessment company Arctic Shores and long time champion of doing assessment right!

Robert and I (and my AI co-host Mayda Tokens!) dig into one of the most urgent problems in hiring right now: the complete breakdown of traditional hiring signals.

We ponder the question- “How do we find the truth in an age where AI has flooded the top of the funnel, made credentials and resumes unreliable, and put enormous pressure on organizations to find new ways to identify talent?”

And we come up with some pretty good answers!

1. The Top of the Funnel Is in Chaos

The numbers are staggering. Accenture’s global resourcing lead told Robert they’re on pace for 12 million applications this year for roughly 100,000 hires — up from 4 million just three years ago. Same size team. Two and a half times the volume. The culprit isn’t a surge in qualified candidates; it’s AI-powered application tools that let candidates apply to jobs while they sleep. The moral contract between candidates and employers has been broken: candidates assume companies are using AI to screen, so they’re using AI to apply.

“It’s chaos out there. Candidates are using AI to fight AI — and we’re in a no-win scenario.”

2. Traditional Assessment Is Increasingly Gameable

Arctic Shores’ research from 18 months ago showed what most people didn’t want to admit: AI can ace virtually any traditional assessment format — personality tests, cognitive reasoning, multiple choice — with ease. And it’s not just about having a second screen open. Candidates can now point a phone at their screen, have the AI read the item, and get the answer instantly. Proctoring doesn’t solve this. The old protection mechanisms are obsolete.

3. The Answer Is Better Signal, Not More AI

The solution isn’t to ban AI from the process — it’s to design assessments that AI can’t easily game because they’re rooted in authentic behavior. Robert’s framework: if AI is being used to evaluate signals, those signals have to be grounded in high-fidelity behavioral data — not scraped from job descriptions, not inferred from keyword matching, not built on garbage in.

Job descriptions themselves are often the first failure point, and no amount of downstream AI sophistication fixes a weak foundation.

4. Stop Counting Leaves — Look at the Roots

Robert’s tree analogy is one of the sharpest frameworks in this episode. For decades, hiring has been obsessed with leaves — the skills on a resume, the credentials on a LinkedIn profile. But with the average shelf life of a skill now estimated at two and a half years, leaves are increasingly unreliable.

What matters is the root system: the durable human capabilities that allow someone to grow new skills, adapt to changing roles, and thrive in uncertainty.

5. Skills-Based Hiring Needs a Clearer Definition of “Skill”

Both Robert and I agree: the skills-based hiring movement is directionally right, but conceptually messy. Calling “innovation” or “persistence” a skill conflates what can be learned with what is innate. Durable traits — personality, cognitive style, learning orientation — don’t expire the way technical skills do. Measurement strategy has to account for these differences, or skills-based hiring just becomes the next echo chamber.

Final Takeaway

The hiring signal crisis is real — and it’s accelerating. AI has made it trivially easy to fake credentials, game traditional assessments, and flood the funnel with noise.

The organizations that receive the best signal won’t be the ones that deploy the most AI. They’ll be the ones that invest in the right signal: behavior-based, validated, and rooted in the durable human traits that no machine can fake.

*Claude.ai assisted with the creation of these show notes

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“By the time you dot the final I’s and cross the final T’s, the assessment is already out of date.”

— Taylor Sullivan

Episode Overview

In this episode I’m joined by rising I/O rockstar Taylor Sullivan, IO psychologist and the architect of Workera’s assessment strategy. With Taylor’s guidance Workera, a verified skills intelligence platform, is doing something most of the industry is still afraid to do: going all in on using AI to build, deliver, and validate AI-based assessments.

Taylor and I (and my AI co-host Mayda Tokens) dig into how this actually works, why it’s scientifically defensible, and why the industry needs to stop waiting and start moving.

Topics Discussed & Key Insights

1. Traditional Assessment Development Is Already Broken

By the time a traditional assessment clears all the I-dotting and T-crossing, it’s often already out of date. AI changes that — enabling dynamic content generation, richer construct understanding, and real-time iteration that keeps pace with how work actually evolves.

2. Codifying Measurement Science Into a Multi-Agent System

Workera didn’t just bolt AI onto existing processes. They embedded IO psychology’s core principles — evidence-centered design, validity frameworks, job analysis — directly into a multi-agent authoring system. Experts define the standards. Agents execute to those standards. The science drives the machine, not the other way around.

Here’s a brief sketch of how it works in practice

• Define the purpose — Tell the agent what you’re measuring and why. This grounds everything that follows.

• Extract the construct — The agent probes the skill space using critical incident techniques, identifying what great performance actually looks like.

• Design the assessment — The agent selects question formats (multiple choice, drag and drop, voice interaction, sequencing) based on what will best elicit evidence of the skill.

• Automated quality review — Before anything goes live, the system checks for bias, language issues, and content alignment to the original skill definition.

• Monitor and improve — Once deployed, the agent tracks response patterns, flags problems, and learns from score appeals adjudicated by humans.

The skill domain is flexible — it works for cheeseburgers or cybersecurity. The methodology behind it is the same either way.

3. The “Harness” — Why This Is Safe

The key to responsible agentic AI isn’t less autonomy — it’s a well-designed harness (the constrained ecosystem where the agents do their thing). Human experts define what good looks like, set quality thresholds, and build in escalation points. The agents work within those constraints and loop back when they hit uncertainty. As Taylor puts it: “It’s not running completely autonomously unchecked.”

4. This Is About Development, Not Just Hiring

Workera’s primary focus is post-hire — workforce development, upskilling, and learning. Once an assessment identifies verified gaps in a person’s skills, the platform connects those gaps directly to personalized learning plans, curating from an organization’s existing content library. Two people can get the same score on an assessment and walk away with completely different development paths based on their specific pattern of strengths and gaps.

5. Verified Skills Intelligence — What It Actually Means

In a world where AI can write a perfect resume and LinkedIn profile for anyone, credentials are noise. Verified skills intelligence cuts through that — using assessment to generate actual evidence of what someone can do, fit for the stakes of the decision being made.

Final Takeaway

The tools to move beyond multiple choice, beyond static assessments, and beyond slow validation cycles exist today. The bottleneck isn’t technology — it’s the will to trust well-designed systems. When the science is built into the machine from the start, speed and rigor aren’t in conflict. They’re the same thing.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“If you know what you’re doing, AI makes you faster. If you don’t, it just makes you wrong faster.”

–Louis Hickman

In this episode I’m joined by esteemed Psych Tech @ Work, Alumnus and AI research machine, Louis Hickman.Our incredible conversation taps into Louis’ myriad research studies to unpack AI’s direct impact on work, domain expertise, and talent assessment.

And of course, this episode also marks the return of the now new and improved AI podcast co-host Mayda Tokens (2.0).

Besides telling dumb jokes-Mayda’s job is to remind us that AI isn’t just a tool — it’s becoming an active participant in how we think, question, and explore ideas.

In the course of our conversation Mayda and I coax some PROFOUND take aways from our friend Louis as he shares the practical outcomes of his research:

1. AI is not removing the need for expertise — it’s making it more visible.

Scaling intelligence is easy.Scaling judgment is not.

The organizations that succeed won’t be the ones that adopt AI the fastest.

They’ll be the ones that:

• Understand what they’re measuring

• Use AI to enhance — not replace — that understanding’

• Maintain control over how decisions are made

2. AI allows us to scale both good science and bad measurement

Louis pushes back on the idea that recent advances represent a fundamental shift in how we measure people. Instead, what we’re seeing is:

• Better models

• Faster processing

• More scalable systems

But none of that replaces the need for valid, reliable, and job-relevant measurement.

3. AI doesn’t level the playing field — it often rewards those who already understand the game.

One of the most interesting ideas in this episode is how AI interacts with individual differences in expertise.

At a high level:

• For simple tasks, AI helps novices perform closer to experts

• For complex tasks, AI actually widens the gap- allowing experts to perform better

Why?

Because experts know how to ask better questions, recognize when AI is wrong, and refine its outputs—while novices often lack the ability to judge quality, diagnose errors, or course-correct when things go off track.

4. Replicability in LLMs Is Possible — if you know how to set it up right

A major “wow” moment in Louis’ research:

By running the model locally on the same class of hardware, fixing the model and prompt, and turning off sampling/randomness in the settings, you can make the system produce the same output for the same input every time.

5. AI should be used to scale decisions, but those decisions still need to be grounded in clearly defined constructs

At this point, AI adoption isn’t optional—it’s expected. Organizations are being pushed to move faster and scale, while vendors are rapidly building and deploying solutions, often without deep validation.

The resolution isn’t to slow down adoption—it’s to ensure we add and maintaining rigor.

1. AI makes it easy to scale assessment, but if the underlying design is weak, we’re just scaling bad measurement faster.

The resolution is to ensure what gets scaled is built on clear constructs, strong design, and validated measurement, so speed amplifies quality—not noise.

7. Working with AI is no longer just about what you can do—it’s about how effectively you can partner to make what you do better!

The tension is clear: AI can accelerate work, but over-reliance without critical evaluation leads to lower quality, missed errors, and reduced trust.

This shows up in real ways—unchecked outputs, declining attention to detail, and growing skepticism in collaborative work.

The resolution is that AI doesn’t replace accountability—users still need to apply judgment, review outputs, and take ownership of the final result.

Tune in to get the full story on these profound revelations and hear Mayda’s stand up comedy routine.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“The rules haven’t changed. The technology has — but the rules haven’t.” — Nathan Mondragon

Episode Overview

In this episode, I’m joined by my old friend (and now co-worker!) Nathan Mondragon, an IO psychologist and long-time leader in creating the future at the intersection of assessment science, hiring technology, and applied AI.

Nathan and I have lived through multiple waves of “this will change everything” technology — from early online testing to video interviewing, machine learning, and now generative AI. And the beat goes on!

Nathan and I have recently joined forces at ProboTalent where we are creating defensible AI based assessment tools.

We talk about where AI has genuinely moved the field forward, where it hasn’t, and why so many of the debates we’re having today are versions of conversations we’ve been having for decades. Along the way, we unpack Nathan’s paradigm busting work at HireVue’, and why the fundamentals of good measurement haven’t changed — even as the tools have.

Topics Discussed & Key Insights

1. The Rules of Good Assessment Haven’t Changed — We Just Keep Forgetting Them

Nathan makes a point that anchors the entire episode: while technology has advanced dramatically, the core rules of good assessment — validity, relevance, interpretability, and fairness — are exactly the same.

AI doesn’t get a pass on methodology. If anything, it raises the bar for rigor, because mistakes scale faster.

2. Early Hiring Tech Was Built to Solve Operational Problems, Not Measurement Problems

We talk about the early days of online hiring and assessment, where the primary goal was digitization, not insight. Systems were designed to move paper processes online, not to improve how well we understand people.

That legacy still shapes today’s platforms — and explains why so many tools feel efficient but shallow.

3. HireVue Was a Real Paradigm Shift — and It Required Scientific Courage

Nathan reflects on the early days of HireVue and why it was genuinely revolutionary at the time. The breakthrough wasn’t just video — it was the larger shift toward digitizing and scaling structured assessment experiences in a way the field hadn’t seen before.

What made this moment interesting from an IO psychology standpoint is that it required a different mindset as a scientist: being willing to engage with a new modality, even when the measurement implications weren’t fully understood yet. Innovation in assessment has always involved tension — between rigor and experimentation, between what’s proven and what’s possible.

Nathan shares what it was like to help lead through that transition, and why thoughtful scientists have to be able to sit with uncertainty long enough to shape new approaches responsibly, rather than rejecting them outright.

4. AI Didn’t Create Bad Measurement — It Made It Easier to Scale

A recurring theme: AI doesn’t magically improve weak constructs. If you feed it noisy proxies, you just get faster, more confident noise.

We discuss why generative AI and machine learning don’t eliminate the need for careful construct definition — and why “it correlates” is not the same thing as “it measures something useful.”

5. Interactivity Matters More Than Modality

One of the most important takeaways: the future of assessment isn’t about whether something is text, video, or simulation-based — it’s about how interactive and information-rich the experience is.

Nathan explains why dynamic interaction reveals far more about decision-making, reasoning, and capability than static prompts ever will.

6. Native AI vs. Embedded AI Is a False Debate

We unpack the difference between “AI-native” products and traditional tools with AI layered on top — and why this distinction often misses the point.

What matters isn’t where AI lives in the stack, but whether it’s being used to improve interpretation, not just automate scoring or classification.

7. Skills and Knowledge Are Still Hard to Measure — and AI Has to Be Used Carefully

We close by confronting a reality the market often underestimates: skills and knowledge testing have always been difficult to do well, and scaling them without losing rigor is even harder.

We connect this directly to the work we’re doing at Probo Talent, where the focus is on a more responsible alternative: using AI to scale the parts of assessment that have historically been hardest to scale, while staying within safe, established modalities and an explainable, scientifically grounded wrapper. The goal is not novelty for its own sake, but a practical example of how AI can be used carefully to solve long-standing problems in skills-based hiring without sacrificing defensibility or trust

Final Takeaway

AI changes how we can build hiring and assessment systems — but it doesn’t change what makes them good.

If we ignore decades of psychological science in favor of speed, novelty, or convenience, AI will simply help us make the same mistakes faster. But if we use it to deepen interaction, improve interpretation, and stay disciplined about what we measure, it has the potential to finally move the field forward in meaningful ways.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“There’s this massive imbalance between the employer side of the recruiting equation where they’ve got all the tech, they’ve got all the weapons… Candidates don’t have anything.”

–Doug Berg

In this episode, I’m joined by Doug Berg, head matcher and big kahuna at Match2, a longtime builder and operator in the talent technology/recruitment space and the only guy I know that wears flip flops to HR Tech..

Doug has lived and hacked nearly every iteration of online hiring — from fax machines and early internet job fairs to today’s AI-powered recruiting chaos.

Doug and I have lived parallel lives in some sense. We have both been on the scene as recruitment went on-line and have continued to wage war against the barriers that are blocking successful hiring. But Doug’s unique experience building recruiting focused tech helps us take a very well rounded perspective.

Doug and I trace the psychology of hiring systems, why most recruiting technology still fails both candidates and employers, and how efficiency-driven design has quietly stripped humanity out of the process.

We talk about what broke, why AI is making some problems worse before it makes them better, and what a candidate-centered future could actually look like if we stop designing hiring like a transactional funnel and start designing it like a relationship.

Topics Discussed & Key Insights

1. Hiring Has Always Been Psychological — Ignoring That Is Why It Breaks

Doug shares early recruiting stories that reveal a core truth: people don’t make job decisions based solely on skills or titles. They’re driven by values, aspirations, lifestyle preferences, and identity. Yet most hiring systems still treat people as static records instead of dynamic humans.

Music to the ears of a psychologist like me!

2. Applicant Tracking Systems Were Built for Control, Not for Candidates

We unpack how applicant tracking systems were designed for compliance and efficiency — not engagement. The result:

• One-way transactions

• Forced applications

• Zero room for curiosity, context, or conversation

Doug explains why this original design choice still haunts modern hiring.

3. AI Isn’t Breaking Hiring — It is Amplifying the Broken Parts

AI didn’t invent hiring dysfunction — it amplified it. Candidates now apply to dozens of jobs at once using bots. Employers respond with more screening, more filters, more automation.

The outcome? More noise. Less signal. Worse experiences on both sides.

4. Real Hiring Happens Through Interaction, Not “Efficiency”

Doug tells stories about simple interventions — like proactive chat on career sites — that led to real hires for impossible-to-fill roles. The lesson is clear: when candidates are allowed to participate instead of comply, hiring actually works.

5. Hiring Will Stay Broken Until Candidates Control Their Side of the System

One of the central ideas in the episode: candidates have never been given real agency. Doug explains the structural imbalance:

• Companies control the systems

• Candidates adapt or disappear

We explore what changes when candidates control their own data, preferences, and relationships — and why that shift matters.

6. The Resume Is a Dead Artifact — Identity Needs to Be Portable

Resumes are outdated snapshots. Doug makes the case for living profiles, portable personalization, and persistent relationships that move with the candidate across employers.

AI finally makes this possible — not by enforcing rigid taxonomies, but by interpreting relevance across skills, experience, and context.

7. The Future of Hiring Should Feel Like Reconnection, Not Rejection

We close by zooming out. Doug shares a simple but radical vision: if someone gets laid off on Friday, they shouldn’t start from zero.

They should already know:

• Who wants them

• What they’re worth

• Where they fit

Hiring shouldn’t feel like rejection roulette. It should feel like an intelligent market reconnecting human supply and demand.

Final Takeaway

Hiring doesn’t fail because people are hard to assess.It fails because we designed systems that ignore how people actually choose, trust, and engage.

AI won’t fix that on its own.But used thoughtfully — with psychology, agency, and dignity baked in — it might finally help us build hiring systems that work for humans again.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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TL;DR

AI literacy is becoming a baseline skill. This episode explores how organizations and individuals are actually building AI capability at work, with a focus on:

• Self-directed learning and AI education at scale

• Personalized learning journeys versus one-size-fits-all training

• The shift from basic AI use to agentic workflows

• The role of human strengths—creativity, judgment, and adaptability—in an AI-driven workplace

In this episode, I’m joined by Erica Salm Rench, an AI educator and leader at Sidecar AI.

Sidecar is an AI education platform and learning management system (LMS) designed to help organizations educate their employees on AI through self-directed learning.

It combines structured courses, role-based learning paths, and hands-on use cases so individuals can build AI capability at their own pace while organizations raise overall AI fluency.

Our conversation explores what AI education actually looks like beyond hype—how people are learning it, how organizations are rolling it out, and why understanding AI is quickly becoming a career differentiator rather than a technical specialty.

AI Education Has Shifted from “What Is It?” to “How Do I Use It?”

Erica explains that the conversation around AI in associations has changed dramatically over the last several years. Early on, organizations were hesitant to even talk about AI. Today, the question is no longer what is AI? but how can we use it to advance our mission, improve operations, and better serve our members?

That shift brings a new challenge: helping people move from curiosity to competence in a way that feels approachable rather than overwhelming.

Meeting People Where They Are

One of the strongest themes in our discussion is the importance of meeting learners at their current level of comfort and knowledge. AI education isn’t one-size-fits-all.

This means combining:

• Foundational AI concepts

• Role-specific applications (marketing, events, operations)

• A growing library of real-world use cases

• Ongoing updates as tools evolve

The goal isn’t to turn everyone into a AI engineer—it’s to help people understand what’s possible and apply AI meaningfully in their day-to-day work.

From Prompting to Agentic Work

We spend time talking about the evolution from simple AI use cases—like writing emails or summarizing content—to agentic AI, where systems take action on a user’s behalf.

This shift matters because it fundamentally changes how work gets done. Instead of just assisting with tasks,

AI begins to:

• Automate multi-step workflows

• Scale work that previously required human labor

• Act as a force multiplier rather than a one-off toolWe agree that while much of this is still clunky today, the direction is clear: agents are becoming a core part of how work will be organized.

Personalized Learning Is the Future of Education

A major insight from the episode is that personalized learning journeys will define the next phase of education—especially in fast-moving domains like AI.

Erica describes how Sidecar uses AI within its learning environment to:

• Act as a learning assistant

• Answer questions in real time

• Reinforce concepts

• Help learners connect theory to application

This mirrors a broader trend: education becoming less about static courses and more about continuous, adaptive support.

The Psychology of Learning AI at Work

We talk openly about fear—fear of job loss, fear of falling behind, fear of not being “technical enough.” Erica makes the case that leaders have a responsibility to educate their teams, not just for organizational performance, but for people’s long-term career resilience.

From a psychological perspective, AI education:

• Reduces anxiety by replacing uncertainty with understanding

• Increases confidence and autonomy

• Helps people see AI as a collaborator, not a threat

Spending even 20–30 minutes a day learning AI can quickly change how people see their own future at work.

Human Strengths Still Matter More Than Ever

One of my favorite parts of the conversation is where we zoom out to the human side of all this. As AI removes technical barriers, the differentiator becomes human qualities—creativity, resilience, judgment, adaptability, and the ability to ask good questions.

AI doesn’t replace these traits. It amplifies them.

Used well, AI allows people to overcome past limitations, work around weaknesses, and bring their ideas to life faster than ever before.

What Listeners Should Take Away

AI literacy is becoming a baseline skill. The people who thrive won’t be the most technical, but the most curious, adaptable, and intentional about learning how to work alongside intelligent systems.

Education—done thoughtfully and continuously—is the bridge between fear and opportunity.

Where to Find Erica

Erica is highly active on LinkedIn and can be found through Sidecar AI, where she and her team are building education-first pathways into AI for associations, nonprofits, and mission-driven organizations.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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Conrad Shaw “So much of the labor market is driven by desperation. UBI shifts that. People can actually hold out for what they’re worth or for work that aligns with who they are.”

— Conrad Shaw

Conrad is perhaps the most unique guest I have had in the 5 year history of this show and he is on to talk about Universal Basic Income (UBI) , a very unique topic that is growing in exposure.

For almost a decade Conrad has dedicated his life and career to furthering the cause of Universal Basic Income (UBI).

In 2016 he and his wife started a documentary called Bootstraps which focuses on following families who lived through the experience of a basic income.

Since then, he has:

• Fundraised for and operated a nationwide basic income pilot

• Filmed a multi-year docuseries currently in post-production

• Co-founded Commingle, a mutual-aid platform enabling communities to self-fund their own grassroots basic income systems

• Worked extensively on messaging, outreach, and public education around income, stability, and societal transformation

I learned a lot from Conrad and our conversation debunked my own myths about UBI. So a really important part of this episode is the truth about what Universal Basic Income (UBI) actually is — and what it is not.

What Universal Basic Income (UBI) Is — And What It Isn’t

UBI is the idea that every person receives a recurring, unconditional, baseline income — a financial floor that ensures no one starts the month at zero. It is not meant to replace work or equalize everybody’s income. Instead, it shifts the starting point so people can make decisions from stability rather than desperation.

What UBI is:

• A stable, universal base-level income for all

• A platform for economic mobility and personal freedom

• A modernized, simplified social safety net

• A tool for reducing the survival-based pressure in the labor market

What UBI is not:

• It does not eliminate jobs

• It does not cap how much people can earn

• It does not remove incentives to work

• It is not a socialist equal-wealth system

UBI reframes the labor market so people compete for work based on interest, alignment, and ability, not raw financial need.

Practical Ways UBI Could Work

Conrad’s work goes beyond speculation. He has spent nearly a decade building practical UBI experiments, including the national pilot documented in Bootstraps (2016) and his current role with the Income To Support All Foundation and Commingle, a new community-driven model.

He explains that UBI can be implemented through several pathways—government programs, private pilots, or community-level mutual aid—but none are simple. A government-led UBI requires political will and rethinking how we allocate resources. Philanthropic pilots can demonstrate impact, but they’re temporary. Community models like Commingle allow people to pool and redistribute resources now, without waiting for legislation, but scaling them is challenging.

What’s clear is that executing UBI at any level is difficult, requiring trust, infrastructure, and cultural acceptance. Yet the difficulty doesn’t diminish the need. Instead, it underscores why experimentation and new models matter.

Individual Differences: Why UBI Supports People Doing What They’re Meant to Do

One of the deepest connections between Conrad’s work and mine is the concept of individual differences—the idea that every person brings a unique constellation of strengths, traits, interests, and abilities that make them naturally better suited to certain kinds of work.

When people are trapped in survival mode, those natural gifts often go unused. They pick jobs they can get, not jobs that reflect who they are. Freedom from this paradigm reshapes careers in ways that benefit both individuals and employers, allowing people to walk away from toxic or exploitative conditions and take jobs they genuinely care about, leading to better performance and engagement.

With a secure foundation, people have the psychological and financial freedom to make career decisions based on fit, not fear. This supports:

• Better alignment between person and role

• Higher engagement and intrinsic motivation

• Better workforce outcomes because people choose work that matches their abilities

• Greater societal value, as more people apply their genuine talents instead of defaulting to whatever job pays immediately

From Conrad’s perspective, this alignment is one of the most compelling aspects of UBI. When people are free to choose work that resonates with their abilities, the labor market becomes more efficient and more human. Employers gain workers who actually want to be there. Individuals gain a sense of purpose rooted in their authentic strengths.

In a world where AI, automation, and job volatility make career paths uncertain, helping people express their natural abilities becomes more important—not less.

How AI Fits Into the UBI Conversation

AI enters this conversation as both a catalyst and a complicating force. As Conrad points out, technological change is accelerating so quickly that we can no longer predict which jobs will exist, which skills will matter, or how stable any given career path will be. This uncertainty puts enormous pressure on individuals—especially those who don’t have the luxury to retrain, take risks, or weather employment gaps. UBI provides a stabilizing infrastructure in that landscape, giving people the freedom to adapt as work evolves rather than being overwhelmed by it.

AI serves the UBI concept well because it highlights the importance of individual differences:

as routine tasks get automated, the value of uniquely human abilities—creativity, empathy, problem-solving, and deep domain expertise—rises.

UBI supports people in discovering and developing those strengths, while also offering society a buffer as AI reshapes industries faster than institutions can respond. In this way, AI doesn’t replace the need for UBI—it makes the case for it even stronger.

Why Making UBI Work Matters in an Uncertain Future

We must acknowledge the reality: we are entering a period defined by instability—rapid technological change, unpredictable job markets, and widening gaps between opportunity and access. In such an environment, the old assumptions about steady careers, stable industries, and predictable pathways no longer hold.

UBI becomes a tool for resilience. It doesn’t solve every problem, but it gives people the space to adapt, learn, and navigate a chaotic future without falling into crisis. It creates room for people to pursue what they’re best suited for, rather than what pays the most simply out of need.

The conversation frames UBI not as a political ideology but as a human-centered adaptation strategy—a way to strengthen psychological well-being, improve labor market alignment, and provide society with a more stable foundation as the world accelerates around us.

The truth is that UBI isn’t easy; it’s a fight against gravity in a system not built for change, but we are entering into an unprecedented level of uncertainty in all aspects of our lives- so we need to have creative and idealistic solutions

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“You can’t implement skills-based hiring by flipping a switch. It’s about changing mindsets, systems, and the language your organization uses to describe talent.”

-Ashley Wallvoord

In this episode of Psych Tech @ Work, me and my AI co-host, Mayda Tokens, welcome fellow I/O psychologist (and LSU Tiger!) Ashley Walvoord, Senior Vice President of Talent at Verizon.

We are joined by my AI co-host Mayda Tokens who continues to impress at times and but showing a tendency to be pretty boring at other times and always telling really bad jokes (I think the API to Chat-GPT 5o gets a very different sense of humor than the consumer version).

I reached out to Ashley after seeing her SIOP presentation about Verizon’s skills based hiring (and organizational transformation) program. Her and her fellow presenters-

Max McDaniel (Verizon)

Christina-Norris Watts (J & J)

Ruth Imose (J & J)

Jason Frizel (Walmart)

provided amazing insights into their company’s’ amazing and inspiring skills based hiring programs.

The hype around skills based hiring these days makes it seem easy. But talk is cheap- and doing skills based hiring right takes a total ALL IN approach. - one that is rooted in the commitment to become a true skills based organization.

Ashley has lived this life and her experience provides an awesome preview of how one of the world’s largest organizations is reimagining hiring and development through skills and AI. We are all lucky to have her on the show!

Verizon’s transformation provides a rare look at how enterprise-scale companies operationalize skills-based hiring while navigating the practical realities of change management, technology integration, and workforce readiness.

Summary

This conversation bridges strategy and execution, offering a clear-eyed view of how a Fortune 50 company is aligning people, process, and technology around skills. Ashley shares the lessons learned from Verizon’s commitment to a multi-year, organization wide transformation. A journey with many whistlestops along the way— from defining skills frameworks to embedding them in hiring and internal mobility.

Key Themes

1. Building Skills Infrastructure at ScaleAshley explains how skills-based hiring starts long before implementation — requiring shared language, governance, and validation across the enterprise. Verizon’s approach focuses on sustainability and integration rather than one-off pilots.

2. Human Oversight in an AI-Driven SystemAI plays a growing role in matching and mobility, but Ashley underscores that human judgment remains central. The goal isn’t automation for its own sake, but augmentation — using technology to help people make better, more equitable decisions.

3. Culture Change Through Data TransparencyVerizon’s success depends on building trust with employees and leaders by showing the “why” behind skills data and AI insights. Visibility into how skills are used for development and promotion helps drive adoption.

4. Enterprise Challenges and Lessons Learned Ashley shares the realities of scaling change: aligning functions, managing vendor relationships, and ensuring consistency across geographies. Her advice is practical — start small, demonstrate impact, and scale what works.

5. Future Vision for Skills and AI in Talent Ashley envisions a future where skills become the connective tissue between learning, mobility, and performance — and where AI acts as a trusted partner in enabling opportunity at every level.

Takeaways

• Enterprise-scale transformation requires governance, not just technology.

• AI can accelerate fairness and insight, but must remain transparent and human-centered.

• Data visibility is the key to cultural adoption — employees must see personal benefit.

• Scaling skills frameworks demands partnership between HR, technology, and business leadership.

The future of work will depend on how we align AI, human judgment, and purpose at scale. And a commitment to verifying and managing skills at scale.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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Quote:

“When this all started (generative AI for the masses), the fear was ‘this is cheating.’ Now we’re flipping the conversation and saying, no — this is actually a skill set you need to develop.”

-Madeline Laureno

In this episode I welcome Madeline Laurano, Founder of Aptitude Research and one of the most trusted voices in HR and TA technology.

With more than 20 years of research and advisory experience, Madeline’s body of work has has tracked the evolution of all things mixing hiring, business, and tech.

We have known one another for a long time and are quite simpatico in our thoughts on talent acquisition, assessment, and skills based hiring.

And we prove it in this show - as we discuss the ins and outs of these crazy times for HR tech, hiring, and of course- AI.

So listen in and take a look into the crystal ball while staying grounded in the truth!

Topics discussed and wisdom dropped include:

1. Why ATS Are Going to Become Extinct

Madeline explained that ATS systems in their current form are not built for the way talent acquisition is evolving. Recruiters are frustrated because ATSs don’t support the workflows or user experience they need, and they will eventually be replaced by more dynamic, integrated platforms that actually match how hiring happens today. Hello AI!

2. What Her Research Says About Skills-Based Hiring

Madeline points out that skills-based hiring is more aspirational than real for most organizations. Aptitude Research has found that companies often treat skills like the old competency models — static, outdated, and resource-intensive — or via an over reliance on AI. Both make it hard to translate into practice without validated frameworks and clean, usable data. The path fwd requires a commitment to strategy, clarity, and validation.

3. How the Fast-Moving Nature of AI Impacts HR Tech Buying

Madeline notes that AI has changed how companies buy HR tech because the market is moving so quickly. In the past, companies would take years to build strategies before investing in technology, but now AI allows them to start much faster — sometimes adopting before they fully understand how to implement, which creates both opportunity and risk. Beware of AI FOMO!

4. Agentic AI and Hiring — What Will the Impact Be?

She described “agentic AI” as a coming wave where AI systems won’t just provide insights but will take autonomous actions. In hiring, this could mean systems that source, screen, and even interact with candidates automatically — raising big questions about oversight, fairness, and how much decision-making organizations are comfortable handing off to machines. Get ready because the rise of autonomous hiring agents is upon us.

5. The Impact of AI on Candidate Experience

Madeline stresses that AI can either improve or damage the candidate experience depending on how it’s implemented. Candidates expect personalization, transparency, and fairness, and if AI-driven processes feel opaque or impersonal, trust will erode quickly — but if designed well, AI can actually enhance communication and responsiveness. We must not villainize AI for this- there is a lot we can do enhance candidate experience and it can actually include the use of AI if done thougthfully.

6. What Will This Look Like 20 Years From Now?

Looking ahead, Madeline predicts that hiring will look radically different in 20 years, with skills-based approaches fully realized and AI deeply embedded into every step of the talent lifecycle. The key difference will be that technology will finally deliver on the vision of matching people to opportunities more accurately, quickly, and fairly at scale.

AMEN- let’s just make sure that people remain in charge!

Check out the episode and learn about the trends from two of the best!

& do yourself a favor and visit Aptitude Research’s website where you can find free access to all of their amazing research!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“Most firms that are using AI are saving two to four hours per week per employee. That’s not transformative. That’s just doing the same thing faster.”

-Alexis Fink

Introduction

In this episode of Psych Tech @ Work, Mayda Tokens (my AI co-host) and I sit down with Alexis Fink, I-O psychologist, long-time HR tech leader at Microsoft, Intel, and Meta, longtime friend and president of The Society for Industrial-Organizational Psychology (aka SIOP)!

Alexis brings decades of experience at the intersection of people, organizations, and technology to the studio, offering a holistic and integrated perspective on the opportunities and challenges of AI in the workplace that is based on reality- not pure philosophy.

We challenge Mayda to hang with us as we talk about all things people, technology, and the future of work. Alexis rocks it. You be the judge of how well Mayda meets the challenge. Hint: like all AI, Mayda is still a work in progress that fails sometimes, while still feeling miraculous IMHO. I mean come on- she speaks in emoji!!!

Alexis leads the charge with her take on these great highlight topics:

1. The Transformation of Knowledge Work AI is reshaping not just factory tasks, but the decision-making and knowledge roles once thought safe from automation.

2. Organizational Design in an AI EraTrue progress requires rethinking workflows so humans and machines complement each other rather than compete.

3. Data Quality and Human-Centered DesignMost raw HR data isn’t fit for AI, making richer, cleaner, and more contextual data essential for real impact.

4. Risk, Accountability, and Quality Control As AI takes on more autonomy, organizations must adapt proven quality management and governance principles to keep it accountable.

5. The Human Problem of AI AdoptionThe hardest barriers to AI adoption aren’t technical but human — fear, resistance, and behavior change.

6. Looking to 2035: The Next-Gen I-O PsychologistFuture I-Os will master AI as a partner, using simulation and immersive tools while keeping work human-centered.

Conclusion

Our conversation underscores a central theme: AI is not even close to perfect and we need to recognize this (Mayda’s responses to our questions are proof of AI gone whack!)

AI’s future in work won’t be defined by algorithms alone, but by how organizations redesign processes, manage risk, and support people through change. For I-O psychologists, HR leaders, and technologists alike, the task ahead is clear — ensure AI is not just bolted onto old systems, but opens opportunities for true collaboration with we humans.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“The problem with AI adoption isn’t just technical—it’s emotional. Creativity lowers the barrier of fear, and that opens the door to skill building.”

– Jimmy Lepore Hagan

Newsflash!

After a much needed hiatus- Psych Tech @ Work is back with a vengeance! During the break I have been heads down in my lab- experimenting and playing with AI.

SHE’S ALIVE!

This episode marks the debut of my self-created AI podcast co-host Mayda Tokens. It took me three weeks to make her and during this process I explored the human side of effectively collaborating with AI. Making Mayda required me to flex my creativity, critical thinking, flexibility and perseverance.

My Mayda experience prepared me firsthand for a great conversation with Jimmy about creativity, AI, and the human psyche.

In this episode of Psych Tech @ Work, I welcome my new friend and fellow New Orleanian Jimmy Lepore Hagan. Together we explore why

creativity is the missing link in many corporate AI readiness programs — and how it can be leveraged to help individuals and teams move from fear to fluency in a rapidly transforming world.

Jimmy brings his bold, experience-driven perspective to the conversation, making the case that creative courage is not a soft skill — it's a strategic asset.

Together, we discuss Jimmy’s new framework for enabling AI adoption through creativity — and my addition to the delivery of his hands-on workshop designedto help HR teams, L&D leaders, and talent professionals build AI fluency through creative exploration.

Summary

Creative thinking isn’t just about making art — it’s about rewiring our brains to embrace ambiguity, take risks, and explore the unknown. In this episode, we discuss how cultivating creativity can de-risk the AI learning curve, helping professionals feel more confident engaging with emerging tools.

In an era of automation, the ability to experiment, play, and fail safely is what separates those who adapt from those who resist.

These traits are not innate — they can be developed, and doing so can radically change how individuals approach new technology.

The episode also highlights a workshop experience that puts this theory into action: a fun, safe, and high-impact program designed to build creative fluency first — and then apply it to AI. This approach helps teams lower psychological barriers to AI experimentation and open the door to real skills development.

I have to give a direct and shameless plug for our workshop. Our workshop — combines science, storytelling, and hands-on exercises to help teams build the mindsets and skills needed for the future of work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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My creative experience building an AI podcast co-host says it all.

Hear all about it on the next episode of the Psych Tech @ Work Podcast - coming soon!

AI skills are essential but daunting

AI adoption is accelerating—over 70% of companies report they’re actively integrating AI tools into their workflows. But for the people expected to use those tools, it’s a different story.

Most professionals say they feel unprepared or even anxious about using AI on the job. Traditional training often falls short with AI skills because it focuses on tools, not mindset.

And the stakes are high: as AI becomes embedded in everyday work, careers will increasingly rely on comfort and expertise with AI.

This gap and the demand for innovative strategies to close it has been top of mind for me. Good news - my fascination with AI led me to a solution! (more on this later)

Creativity unlocks AI skills

I recently gave a talk at a meeting of the New Orleans AI Philosopher’s group (AKA NOAI), on AI and the future of our local economy.

At this event I saw a talk by Jimmy Lepore Hagan—an artist, designer and educator—who shared a fascinating approach to AI adoption that is fresh, unique, and noteworthy.

Jimmy’s talk was about the value of creativity in lowering fear of AI. He demonstrated some concepts from a workshop series he has developed featuring a series of low stakes, creative exercises grounded in design thinking to help people build comfort, confidence, and curiosity when working with AI.

As a workplace psychologist I immediately saw the potential for a collaboration - applying Jimmy’s hands-on educational model to my world to help people leaders solve a difficult problem.

As someone who’s spent decades applying psychological science to the development and measurement of human traits in the workplace, I have experience understanding the impact of creativity on outcomes that are directly related to work performance.

As I processed this stuff- I took a step back and reviewed foundational research that shaped my earlier work—this time, through the lens of AI. The connections stood out immediately. Traits like divergent thinking, cognitive flexibility, and creative self-efficacy have long been linked to performance, but they also play a critical role in how people approach new, uncertain technologies. The evidence is clear: creativity and experiential learning do more than build skills—they tap into deeply human strengths that make people more open, adaptable, and ready to thrive in the face of change.

My dance with AI says it all

It became pretty clear to me that a collaboration with Jimmy could really have some legs.

To get the ball rolling I invited Jimmy to be a guest on my Podcast “Psych Tech @ Work”.

To prepare I wanted to gain some first hand experience with using creativity to help me sharpen my AI skills.

I suck at coding and the requirement to use Python for this definitely gave me some anxiety, but I knew ChatGPT could somehow have my back.

Thus came the idea to challenge myself (and have some fun) building an AI podcast co-host, Mayda Tokens.

Mapping out and executing a workflow to bring Mayda to life threw me plenty of curveballs. Some of ChatGPT’s more noteworthy and frustrating shenanigans included:

• Multiple times ChatGPT relentlessly tried, and continually failed, to solve technical issues; but would not give up until I suggested that we were going in circles in a blind alley and maybe we should explore alternative methods. This prompt led immediately to a set of viable alternatives that would never have been explored if I hadn't decided to pull the plug.

• When I backed ChatGPT into a corner I was flabbergasted when, instead of hallucinating a solution or looking for another option, it simply refused to help me. This was a head scratching result that must have exposed a ghost in the machine because its prime directive is NEVER to say NO!

• As I explored different options for Mayda’s voice, my text to speech output randomly switched to Japanese and then to emoji

• As we hit dead ends trying to figure out how to bring Mayda into my podcast studio, I stupidly followed its instructions to run to Best Buy and Guitar Center to buy unnecessary hardware that neither place actually sold.

In the three weeks it took to bring Mayda to life, I became hyper-focused—borderline obsessed—with working through many obstacles. The dopamine hits I got each time we solved a challenge together reminds me that my brain chemistry is essential for accessing and applying uniquely human traits like creativity, critical thinking, resilience, and tolerance for ambiguity.

The interplay between my human biology and psychology was essential for winning the day, and my experience building Mayda really hammered home the value of creative collaboration with AI.

Our workshop is the gateway to fearless AI skills

Learn how we’re helping companies build fearless, AI-ready teams.

Viewing AI as a dance partner is the paradigm that serves as the foundation of our workshop. Instead of lectures, videos, and formulaic exercises; we use creative, hands-on activities that help people relate to AI in a way that feels playful, safe, and real.

In our workshop participants explore AI through:

• Improvised dialogue with generative models

• Creative prompt challenges

• Group problem-solving sprints

• Human-AI art collaborations

• Guided reflection and peer feedback

By mapping each of these design thinking centric, hands-on exercises to psychological principles—like creative self-efficacy, openness to experience, and experiential learning—the workshop becomes more than fun. It becomes a stealth learning experience where participants not only gain essential AI skills, they undergo cognitive changes that empower them to believe in the value of partnering with AI.

We believe our workshop can be a difference-maker for companies navigating AI transformation—and a real competitive advantage for those that are bold enough to think differently about AI adoption.

To learn more about our workshop, the collaborative ideas behind it, and meet Mayda Tokens Visit our workshop page and be sure to listen to our conversation about it on the next edition of my Psych Tech @ Work podcast.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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Guest: Christine Boyce, Global Innovation Leader at ManpowerGroup/Right Management

“We have to stress-test innovation in the messiness of real-world hiring, not just ideal lab conditions.”

-Christine Boyce

In this episode of Psych Tech @ Work, I’m joined by my longtime friend Christine Boyce, Global Innovation Leader at ManpowerGroup/Right Management, to explore how innovation — especially around AI — is reshaping hiring and talent development at scale, and why solving for trust, transparency, and operational realities matters more than ever.

Summary

At the heart of this conversation is the reality that scaling AI innovation in hiring brings massive complexity. While AI offers incredible promise, solving for accuracy, fairness, and operational reality becomes exponentially harder when you're dealing with a large number of unique clients.

Christine Boyce, through her work at ManpowerGroup & Right Management, operates at the intersection of these challenges every day. Unlike internal talent acquisition leaders who focus on one organization's needs, Christine must help innovate across a vast client portfolio. Each client presents different barriers — from data limitations, to ethical concerns, to regulatory pressures — and innovation must be modular, defensible, and adaptable to succeed.

This vantage point gives Christine a unique, big-picture view of how AI adoption really plays out across industries and markets.

We dive into the practical challenges of innovating responsibly: earning trust, scaling solutions across diverse environments, and balancing speed with fairness. Christine’s work at ManpowerGroup & Right Management highlights how innovation must be deeply disciplined if it is to achieve true scale and impact.

The Core Challenge: Scaling Accuracy and Fairness

At the heart of using AI for hiring lies the challenge of achieving accuracy and fairness at scale. AI’s true value isn’t just its ability to make individual decisions — it’s in processing vast amounts of data and automating judgment across thousands of candidates. However, scale magnifies both strengths and weaknesses: minor biases can grow into systemic problems, and small inefficiencies can snowball into major failures.

Staffing firms like ManpowerGroup offer critical real-world lessons:

• Scale forces discipline — Every AI tool must be rigorously vetted for fairness, transparency, and defensibility before deployment.

• Real-world variation stresses the system for the better — Tools must flexibly adapt to diverse jobs, industries, and candidate pools. This makes the overall path of innovation better and drives great learnings across the board.

• Speed must not erode trust — Productivity gains must still respect ethical standards and candidate experience.

• External accountability keeps AI honest — Clients demand transparency, validation, and explainability before adoption.

Real Barriers to AI Adoption: What Clients Are Facing

Despite AI's potential, Christine identifies several persistent hurdles that she faces when serving her diverse slate of clients:

• Resistance to Behavior Change: Even demonstrably valuable AI tools often struggle against entrenched workflows and distrust of automation.

• Ethical and Trust Concerns: Clients demand AI systems that are transparent, explainable, and defensible, fearing reputational or regulatory risks.

• Vendor Noise Overload: Saturation by "AI-washed" vendors makes it hard to differentiate true innovation from hype.

• Mismatch Between Hype and Practical Needs: Clients need tools that solve today’s operational problems — not just futuristic visions disconnected from reality.

• Fear of Creeping AI Adoption: Organizations worry about AI capabilities being embedded into systems without visibility or intentionality.

• Compliance and Regulation Anxiety: Global and local regulations (like the EU AI Act or pending US laws) create urgency for proven, compliant AI solutions.

• Talent Data Readiness: Without clean, structured internal data, even the best AI solutions struggle to deliver meaningful results.

These challenges aren't isolated — they reveal the broader realities companies must manage when trying to adopt AI responsibly at scale.

Ultimately, client concerns have a hand in AI innovation because they are critical for the adoption of these technologies, shaping how staffing firms and vendors must design, validate, and deploy solutions.

There’s an inherent tension between the drive for scale and the need for trust, fairness, and operational reality.

Christine’s experience demonstrates that true innovation in AI for hiring isn't just about introducing new tools — it’s about creating resilient, transparent systems that can adapt to real-world complexity. Managing the tension between speed, scale, trust, and fairness represents the path to a bright future.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"Part of putting an AI strategy together is understanding the limitations and where unintended consequences could occur, which is why you need diversity of thought within committees created to guide AI governance and ethics."

– Bob Pulver

My guest for this episode is my friend in ethical/responsible AI, Bob Pulver, the founder of CognitivePath.io and host of the podcast "Elevate Your AIQ."

Bob specializes in helping organizations navigate the complexities of responsible AI, from strategic adoption to effective governance practices.

Bob was my guest about a year ago and in this episode he drops back in to discuss what has changed in the faced paced world of AI across three pillars of responsible AI usage.

• Human-Centric AI

• AI Adoption and Readiness

• AI Regulation and Governance

The past year’s progress explained through three pillars that are shaping ethical AI:

These are the themes that we explore in our conversation and our thoughts on what has changed/evolved in the past year.

1. Human-Centric AI

Change from Last Year:

• Shift from compliance-driven AI towards a more holistic, human-focused perspective, emphasizing AI's potential to enhance human capabilities and fairness.

Reasons for Change:

• Increasing comfort level with AI and experience with the benefits that it brings to our work

• Continued exploration and development of low stakes, low friction use cases

• AI continues to be seen as a partner and magnifier of human capabilities

What to Expect in the Next Year:

• Increased experience with human machine partnerships

• Increased opportunities to build superpowers

• Increased adoption of human centric tools by employers

2. AI Adoption and Readiness

Change from Last Year:

• Organizations have moved from cautious, fragmented adoption to structured, strategic readiness and literacy initiatives.

• Significant growth in AI educational resources and adoption within teams, rather than just individuals.

Reasons for Change:

• Improved understanding of AI's benefits and limitations, reducing fears and resistance.

• Availability of targeted AI literacy programs, promoting organization-wide AI understanding and capability building.

What to Expect in the Next Year:

• More systematic frameworks for AI adoption across entire organizations.

• Increased demand for formal AI proficiency assessments to ensure responsible and effective usage.

3. AI Regulation and Governance

Change from Last Year:

• Transition from broad discussions about potential regulations towards concrete legislative actions, particularly at state and international levels (e.g., EU AI Act, California laws).

• Momentum to hold vendors of AI increasingly accountable for ethical AI use.

Reasons for Change:

• Growing awareness of risks associated with unchecked AI deployment.

• Increased push to stay on the right side of AI via legislative activity at state and global levels addressing transparency, accountability, and fairness.

What to Expect in the Next Year:

• Implementation of stricter AI audits and compliance standards.

• Clearer responsibilities for vendors and organizations regarding ethical AI practices.

• Finally some concrete standards that will require fundamental changes in oversight and create messy situations.

Practical Takeaways:

What should I/we be doing to move the ball fwd and realize AI’s full potential while limiting collateral damage?

Prioritize Human-Centric AI Design

• Define Clear Use Cases: Ensure AI is solving a genuine human-centered problem rather than just introducing technology for technology’s sake.

• Promote Transparency and Trust: Clearly communicate how and why AI is being used, ensuring it enhances rather than replaces human judgment and involvement.

Build Robust AI Literacy and Education Programs

• Develop Organizational AI Literacy: Implement structured training initiatives that educate employees about fundamental AI concepts, the practical implications of AI use, and ethical considerations.

• Create Role-Specific Training: Provide tailored AI skill-building programs based on roles and responsibilities, moving beyond individual productivity to team-based effectiveness.

Strengthen AI Governance and Oversight

• Adopt Proactive Compliance Practices: Align internal policies with rigorous standards such as the EU AI Act to preemptively prepare for emerging local and global legislation.

• Vendor Accountability: Develop clear guidelines and rigorous vetting processes for vendors to ensure transparency and responsible use, preparing your organization for upcoming regulatory audits.

Monitor AI Effectiveness and Impact

• Continuous Monitoring: Shift from periodic audits to continuous monitoring of AI tools to ensure fairness, transparency, and functionality.

• Evaluate Human Impact Regularly: Regularly assess the human impact of AI tools on employee experience, fairness in decision-making, and organizational trust.

Email Bob- bob@cognitivepath.io

Listen to Bob’s awesome podcast - Elevate you AIQ

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“We have to move beyond the idea that a skills-based job description is enough—there needs to be validation, assessment, and a clear pathway for job seekers to prove their abilities.”

-Jason Tyszko

In this episode of Psych Tech @ Work, I sit down with Jason Tyszko, Senior Vice President of the U.S. Chamber of Commerce Foundation, to discuss what it really takes to make skills-based hiring a reality.

Jason oversees the Foundation’s T3 Innovation Network, a public-private initiative aimed at creating a more equitable and inclusive job market. T-3 focuses on using digital tools to improve communication between different parts of the job market, ensuring that all learning is recognized and valued. T-3’s mission to bridge gaps between employers and workers via the advancement of skills-based hiring makes Jason one of the world’s foremost authorities on the subject.

Our conversation is a must for anyone interested in understanding the REALITIES required for true skills-based hiring. Most conversations on the subject are more hype than substance, but not this one! Jason takes us deeper into the reality of what it will take to make skills based hiring more than just an empty buzzword.

To ground our conversation in a dose of reality, Jason boils success with skills based hiring into these three pillars.

• Interoperable Skills Data

• To make skills-based hiring a reality, we need standardized, structured, and widely accepted skills data that flows seamlessly across education providers, employers, and workforce systems.

• Without interoperability, skills data remains fragmented, making it difficult for employers to assess candidates meaningfully.

• Employer Engagement and Adoption

• Employers must align job descriptions, hiring processes, and internal mobility pathways around skills rather than degrees or traditional credentials.

• Many organizations support skills-based hiring in theory but fail to implement it fully due to ingrained legacy practices.

• Technology Infrastructure and Ecosystem Readiness

• AI, job-matching platforms, and hiring tools must be built to recognize and evaluate skills accurately, rather than simply filtering candidates based on outdated proxies like job titles or degrees.

• Systems should support skills validation, assessment, and transparent career pathways to ensure fair and effective hiring decisions.

Jason explains how these pillars support and enable five critical but often overlooked elements that are essential to making skills-based hiring work:

1. Learning and Employment Records (LERs) & The LER Resume Standard

• What it is: LERs are digital, verifiable records of a person’s skills, training, certifications, and work experience. Instead of relying on traditional resumes or self-reported skills, LERs allow employers to see a structured, validated record of a candidate’s capabilities.

• Why it matters: Today’s hiring systems don’t talk to each other. Skills data is trapped in different platforms (learning management systems, certifications, HR software). LERs allow skills-based hiring to function at scale by ensuring a candidate’s credentials are portable and universally recognized.

• LER Resume Standard: This is a newly developed resume format built to process LERs, ensuring HR tech systems can read, compare, and use skills-based data more effectively.

2. Durable Skills

• What it is: Unlike technical skills (which can quickly become outdated), durable skills are long-lasting, transferable skills like critical thinking, adaptability, leadership, and collaboration.

• Why it matters: Most AI-driven hiring tools over-prioritize technical skills, but durable skills are what truly drive career success. Without a way to assess and validate them, companies risk hiring for short-term needs instead of long-term potential.

3. The Interoperability Layer

• What it is: A technical framework that allows skills data from different platforms to connect and work together—like an API that helps job boards, HR systems, and learning platforms “speak the same language.”

• Why it matters: Right now, skills-based hiring is fragmented because every company and HR tech provider uses different skills taxonomies and formats. An interoperability layer standardizes how skills data is shared, making it easier for employers to evaluate candidates based on a common skills framework.

4. Employer-Led Recognition

• What it is: A system where workers’ skills are validated by their employers and colleagues, not just through certifications or formal education. This could involve peer endorsements, manager assessments, or internal training validations.

• Why it matters: Most skills-based hiring focuses on externally validated credentials (e.g., certificates, degrees), but many people develop critical skills on the job. Without a structured way to recognize and verify these skills, businesses overlook talent that is already in their workforce.

5. Skills Wallets

• What it is: A digital, user-controlled repository where individuals can store, manage, and share verified records of their skills, credentials, and learning experiences.

• Why it matters: Unlike traditional resumes or degree transcripts, Skills Wallets give workers full ownership of their skills data, making it portable across jobs, industries, and learning platforms. This enables lifelong learning and career mobility in ways that existing hiring systems do not support.

Skills-based hiring has the potential to transform the workforce, but it won’t succeed without system-wide changes in HR technology, workforce data, and employer incentives. Jason’s insights reveal the often-ignored challenges and solutions that can make this shift truly scalable and effective. If you’re in talent strategy, workforce development, or HR technology, this episode provides a realistic roadmap for making skills-first hiring work.

• Learn more about the T3 Innovation Network: t3networkhub.org

• Contact Jason

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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In our recent LinkedIn Live session my esteemed colleague, Neil Morelli, founder of Workplace Labs, and I present a philosophical but practical approach to the adoption of HR Tech tools.

Check out the full video of the presentation attached to this post and our accompanying slides (found at the bottom of the post).

Here is a quick overview of the ideas that form the foundation of the presentation.

“The highest-level goal of the talent acquisition (TA) function is to ensure that an organization has the right people, in the right roles, at the right time, to drive business success.”

-Chat GPT 4o & your hosts’ combined 50 years of experience

Talent leaders are feeling the pressure to execute

Modern hiring problems such as resource constraints, candidate scarcity and overload, the move to skills based hiring, and avoiding bias have talent leaders feeling the pressure to find fast solutions!

Relieving these pressures often create a temptation to put tools before strategy. AI is a great example of this.

The stakes are high, and AI offers a compelling solution- or does it?

AI is complex and making decisions about it requires a strong foundation of knowledge and careful planning.

In this presentation we discuss 4 common mistakes in the adoption of HR tech, with a focus on AI tools (are there any other types these days?).

We discuss how a tools first mentality is often the root cause of these four common mistakes and offer guidance on how to avoid them.

1. Missing AI’s ‘creeping normality':

As technology becomes more entrenched in your processes and vendors add new functionalities that are accessible, adoption often occurs with little oversight or consideration. When it comes to solving problems related to talent supply or overload, AI recruitment platforms are increasingly embedding “talent matching” functionalities that create risk without any substantial rewards.

2. Chasing Skills Without Definition or Direction:

We can all agree that skills based hiring has merit. But it requires alignment on what a skill means to your organization and a holistic view of where they matter and why. Merely removing resumes from the evaluation process or adopting tools, AI or otherwise, that claim to support skills based hiring without a holistic strategy is a dead end street.

3. Failing to evaluate your firm’s culture and climate for adopting AI based tools:

There is a maturity required for the successful adoption of AI based tools. Understanding your firm’s readiness for AI based tools, and ensuring that you are ready to go all in is essential. Education on, and knowledge of, AI across the entire organization is a big part of successful adoption.

4. Letting vendors dictate strategy and adoption:

Most vendors do offer products that can have an impact, and their messages make it tempting to jump right in. Before biting on a shiny new object, adoption of any AI based tool should be pre-empted by a house made strategy. Vendors must be held to a standard evaluated by domain experts using a framework built on the principles of ethical and effective use of AI.

At the end of the presentation we provide a case study that probably feels pretty relatable to any talent acquisition professional. Here we tell a story of how mistakes are made and provide insights to help create the awareness needed to avoid them.

No one is perfect - but AI alone will not create perfection. Keeping things in perspective and a thoughtful and methodical process that is not driven by fear is essential to the successful adoption of AI technologies.

Download our slides here

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"The hiring industry is at a breaking point—AI is putting pressure on old systems that were never designed for this level of automation."

–Jeff Taylor

In this episode of Psych Tech @ Work, I am joined by Jeff Taylor, serial entrepreneur and founder of Monster.com, & Boomband a revolutionary new platform that is looking to turn hiring on its ear.

Few people have shaped the hiring industry as profoundly as Jeff, whose vision transformed job search from a niche experiment into an industry standard. Jeff’s journey—from building the first large-scale job board to continuously innovating in the talent acquisition space—gives him a unique perspective on where hiring technology has been and where it’s headed, making him the perfect guest to explore the next big disruptions in talent acquisition and how AI is reshaping the hiring process..

In our time together we reminisce about the story behind Monster’s memorable Superbowl ads. (Who can forget the kid saying: “I want to claw my way up to middle management!” ) and the formative impact my job at Monster (circa 2000) has had on my career.

But enough about me! Our conversation explores the rapid acceleration of AI in recruiting, from automating sourcing and matching to the potential risks of AI-generated applications flooding hiring systems.

Jeff happily shares his candid thoughts on why hiring technology has stagnated, how AI is creating new challenges for recruiters, and what companies must do to stay ahead in an increasingly automated hiring landscape. We also discuss the core concepts behind Boomband, Jeff’s new social hiring platform.

Topics Covered:

• Monster.com’s origin story and how it transformed hiring and created the “job board” industry.

• The shift from traditional job search to AI-driven sourcing and candidate matching and what this means for the future of hiring.

• The pros and cons of AI-generated resumes and job applications—are we heading toward an overload of unqualified applicants?

• The failure of legacy hiring systems to keep up with modern job-seeker behavior.

• The potential for AI to create more personalized and predictive hiring experiences and Boomband Jeff’s new venture that is focused on creating a new paradigm for hiring (again!).

Takeaways:

• Job boards revolutionized hiring—but they haven’t evolved fast enough. The core concept of posting jobs and waiting for applications hasn’t fundamentally changed in decades.

• AI is making job search more efficient but also more chaotic. Automated resume generation and mass applications are overwhelming recruiters and breaking traditional applicant tracking systems.

• Legacy hiring technology is struggling to adapt. The demand for AI-powered sourcing and skills-based hiring is exposing the limitations of old-school job posting and resume-matching platforms.

• The next frontier of hiring is predictive and personalized. Jeff envisions AI-driven career pathing, real-time job market intelligence, and new ways to match candidates based on abilities, not just experience.

Jeff’s perspective on AI-driven hiring, the changing nature of job search, and where hiring technology must go next makes this conversation a must-listen for anyone interested in the future of work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“The future of assessments is about customization at scale. AI allows us to generate and adapt assessments in real-time, making them more relevant to specific job roles.”

–Ben Williams

Introduction:

In this episode of Psych Tech @ Work, I sit down with Ben Williams, Managing Director of Sten 10, to discuss how AI is reshaping the field of psychometric assessments and hiring processes. Our conversation dives into the evolving landscape of AI-driven assessments, the ethical considerations of using AI in hiring, and the challenges of maintaining transparency and fairness while incorporating new technologies. Ben shares insights into blending AI with traditional assessment tools and how this impacts the future of selection processes.

Key Topics Covered:

• The role of AI in automating and customizing assessments

• Emerging challenges in trust, fairness, and explainability in AI-powered hiring

• The importance of designing job-specific psychometric tools that align with organizational needs

• AI's potential in generating, scoring, and validating assessments

• Future implications of AI on entry-level and senior hiring roles

Summary:

We explore AI’s role in streamlining psychometric assessments while addressing challenges in maintaining transparency and fairness. Ben describes how Sten 10 has integrated AI to make assessment processes faster and more personalized without losing the critical human oversight needed for ethical hiring practices.

We also discuss prompt engineering, AI literacy, and the limitations of AI-generated assessments. One significant takeaway is the growing importance of designing highly contextual and customized assessments using AI while ensuring they remain interpretable and meaningful.

We touch on real-world examples, including how AI can generate coaching tips and personality profiles, as well as potential concerns regarding the over-reliance on AI outputs. The conversation also highlights emerging roles related to AI governance and the need for regulatory oversight to ensure fair hiring practices.

Key Takeaways:

• AI augments, but doesn’t replace human oversight: While AI is making assessments faster and more scalable, the need for human validation remains critical to ensuring fairness.

• Custom psychometric assessments are the future: Moving beyond off-the-shelf tools, companies can develop highly specific and job-relevant assessments using AI.

• Prompt engineering for assessments: Organizations can create better assessment tools by focusing on AI prompt development and optimization.

• AI literacy is essential for hiring professionals: As AI becomes more embedded in hiring, HR professionals need to understand its benefits and limitations to apply it responsibly.

• Trust and explainability are key: Companies must prioritize transparency to gain candidate trust and meet regulatory standards.

Conclusion:AI’s role in hiring is evolving rapidly, and the opportunities for innovation are endless. However, as Ben notes, the path forward requires a careful balance between technological advances and maintaining human control. By designing psychometric tools with AI and human collaboration, organizations can achieve a fairer and more effective hiring process.

Take It or Leave It? Articles:

• “Ineffective Human-AI Interactions and Solutions” — Oxford Review

• Summary: This article delves into the factors influencing human-AI collaboration, including cognitive load and decision control. Ben highlights how integrating AI into familiar tools like Slack and Word can reduce friction and improve adoption.

• “AI and Public Perception: What Americans Really Think” — Center for Data Innovation

• Summary: A survey reveals mixed feelings about AI, with curiosity decreasing and negative emotions on the rise. Ben critiques the contradictions in public attitudes toward AI and how these perceptions could shape its future adoption in hiring.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“We need global standards to define and verify skills, or we’ll be left with confusion and inconsistency across industries.”

-Notebook LM’s Deep Dive Podcast Hosts

Skills based hiring is all the rage, and so is AI.

So what happens when you mix the two?

In this special edition of Psych Tech @ Work, I handed the mic over to AI using Google’s Notebook LM. The result? A fully AI-generated exploration of the evolving world of skills-based hiring. But how well did AI do at covering this complex and nuanced topic?

So how did it do? Listen and decide for yourself.

In the meantime- Here is a short summary to pique your curiosity.

Skills-based hiring promises to break down barriers and redefine how we think about qualifications, but it’s not without challenges. This episode examines how companies can move beyond traditional degree requirements and leverage diverse learning pathways. It also highlights the shift from career ladders to flexible, lattice-like models and the critical role of leadership in making these transformations happen.

Key Takeaways:

• AI is a tool, not the solution: Organizations need both AI-driven assessments and human judgment to effectively identify and verify skills.

• Degrees aren’t everything: Employers must embrace non-traditional education pathways to access untapped talent.

• Lifelong learning is essential: Workers should continuously upskill and showcase their abilities through portfolios and personal branding.

• The career ladder is outdated: Flexible career paths based on transferable skills are the future.

• Leadership drives change: True transformation in hiring practices requires bold decisions beyond tech implementation.

Conclusion:

This AI-powered episode demonstrates the potential of using AI for content creation while also showing its limitations. AI did a great job providing structure and highlighting key points, but human oversight remains essential to ensure deeper exploration and address the human factors that technology alone can’t fully capture. Skills-based hiring requires more than AI—it needs leaders willing to rethink and redesign hiring practices with empathy and inclusivity in mind.

Please listen and share your thoughts on how these robots did exploring the issues and drawing meaningful conclusions!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“Technology should enable human connection, not replace it.”

—Alison Eyring

Introduction

In this episode, I’m joined by my friend Alison Eyring, an IO psychologist with decades of experience in the realm of global leadership and talent development and the founder of Produgie.

I have known Alison for a very long time - in fact she was my first “real world” project sponsor back in 1994!

It was a pleasure to welcome Alison to the show for a great conversation about the role of human centered design when building software to help leaders do their thing!

Summary:

Our conversation explores the intersection of leadership psychology and technology design. Alison shares insights on how psychological safety can be both measured and improved, emphasizing its critical role in team dynamics and organizational success. We dive into her approach to developing tools tailored to user needs, the importance of cultural agility for global leaders, and how technology can both enhance and challenge workplace trust. Throughout, Alison highlights how organizations can foster meaningful change through a combination of data, design, and human connection.

Key Topics Covered:

• The psychology behind software usability and human-centered design.

• Measuring and improving psychological safety within teams.

• The evolving role of AI in leadership and organizational development.

• Using adaptive tools to support leaders in achieving greater impact.

• The challenges and opportunities of cultural agility in a globalized workforce.

Takeaways:

• Psychological Safety: Leaders can actively improve psychological safety, a critical element for team effectiveness and engagement, by fostering trust and transparency.

• Cultural Agility: Leadership in a global context requires a combination of self-awareness, competencies, and experiences to navigate cultural differences effectively.

• Data-Driven Insights: Organizations can gain actionable insights from assessments and development tools to better understand leadership strengths and weaknesses.

• Human-Centered Design: Building technology for HR or leadership should prioritize the user’s challenges and needs, not just the buyer’s demands.

• AI in Leadership: AI can support leaders in providing feedback, fostering growth, and driving measurable outcomes, but its use must be transparent and human-supervised.

Take It or Leave It Articles:

• “The Homework Apocalypse” by Ethan Mollick

• Summary: This article discusses how educators are grappling with AI tools used by students for coursework and the need to rethink educational approaches. Alison critiques the rapid pace of AI developments and emphasizes the importance of teaching judgment and understanding bias in AI-generated insights.

• “Psychological Safety in the Workplace”

• Summary: This article explores what psychological safety is, what erodes it, and how organizations can foster it. Alison highlights the timeless nature of this topic and its centrality to leadership and organizational success.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"AI isn’t replacing people—it's augmenting them. The people who know how to use AI will replace those who don’t: "Redesigning jobs is about understanding which tasks humans excel at and which tasks AI can handle—then finding the perfect balance."

Guest:

Sania Khan

• Labor Economist, founder of Inflection Point Consulting, Chief Economist at a leading talent intelligence AI company, Senior Economist at the Bureau of Labor Statistics (BLS)

Summary:

In this episode of Psych Tech @ Work, I welcome a new friend and brilliant Labor Economist Sania Khan, whose unique perspective blends macroeconomic labor trends, AI-driven work redesign, the evolution of skills, and the future of work

Sania shares insights from her experience at the Bureau of Labor Statistics and her work with emerging talent intelligence tools to tackle one of today’s hottest topics: how jobs are being fundamentally deconstructed into tasks, skills, and competencies.

We dig into how AI is reshaping work—from automating routine tasks to creating new opportunities—and what this means for businesses and individuals. Sania makes the case for job redesign as an essential forward looking strategy for organizations as they adapt to the increasing role of AI in redefining the rules of work.

We agree that the world of work will increasingly find itself tied to a skills based economy which will require solving the challenge of moving beyond buzzwords like “skills-based hiring” and focus on aligning emerging technologies with human potential.

This will require building consistent skills taxonomies, focusing on durable skills like problem-solving and critical thinking, and the gap between hype and reality when it comes to AI’s impact on the labor market.

Topics Covered:

• Deconstructing Jobs with AI

• How AI is automating tasks within jobs, freeing workers for more meaningful work.

• The importance of job redesign to align organizational goals with evolving roles.

• Skills-Based Hiring and Skills Taxonomies

• Why a globally accepted definition of "skills" remains elusive and how this hinders interoperability across platforms.

• The challenge of relying on resumes and job descriptions as source materials for skills analysis.

• The Future of Work and AI's Impact

• AI’s dual role: creating efficiencies while raising concerns about job replacement.

• Predictions for future jobs—like AI specialists, prompt engineers, and responsible AI officers—and how organizations can prepare.

• Durable Skills and Adaptability

• Why “durable skills” like problem-solving, critical thinking, and agility will define professional success.

• How workers can future-proof their careers by learning to work with AI, not against it.

Takeaways:

• AI is reshaping work by automating routine tasks, but humans remain critical for complex, meaningful roles.

• Organizations must focus on job redesign to capitalize on AI while ensuring employees do meaningful, value-added work.

• Skills-based hiring is promising but hindered by inconsistent taxonomies and unreliable data sources.

• Durable skills—like critical thinking, problem-solving, and adaptability—are the key to navigating AI-driven change.

• Workers who learn to augment their skills with AI will have the greatest advantage.

• New roles like AI specialists, responsible AI officers, and prompt engineers will emerge as businesses adopt more advanced technologies.

Articles Discussed in the "Take It or Leave It" Segment:

• "Research: How GenAI is Already Impacting the Labor Market" – Harvard Business Review

• Summary: A data-backed look at how generative AI is reducing demand for automation-prone gig work while increasing competition in the labor market. Sania underscores the importance of becoming exceptional at your craft to remain competitive.

• "How AI Is Fueling Long-Term Job Growth" – Fast Company

• Summary: A positive perspective on AI’s role in creating new jobs, like AI specialists and data scientists. Sania challenges the overly optimistic forecasts, noting the need for realistic strategies to align skills with emerging roles.

This episode provides a compelling look at the intersection of technology, skills, and workforce transformation—a must-listen for leaders navigating the evolving world of work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"The most important competency for success in global assignments? Humility—being willing to learn how to succeed in a new cultural context."

• Paula Caligiuri

Paula is THE expert in this realm!

In this episode, I welcome Paula Caligiuri, a renowned expert in cross-cultural psychology and global leadership and author of many books about cross-cultural adaptation and career happiness, the latest ones being:

Build Your Cultural Agility: The Nine Competencies of Successful Global Professionals (2021)

Live for a Living: How to Create your Career Journey to Work Happier, Not Harder (2023, co-authored with Andrew Palmer)

I have known Paula for almost 30 years. Her research played an essential role in my dissertation which was on cross-cultural adaptation in expatriate work assignments. While I do not work in this area, Paula sure does! She has dedicated her career to research and practice on the psychology of cross-cultural adaptation in both the personal and professional realms.

I really enjoyed the opportunity to speak with Paula about the intricacies of cultural agility, the challenges faced by individuals working internationally, and how organizations can better prepare their employees for success in diverse environments.

Cultural Agility is the name of the game.

Our conversation is anchored by the concept of “Cultural Agility”, a combination of awareness, competencies, and experiences that allow individuals to be effective in multicultural environments.

Paula describes it as being made up of:

• Awareness: Understanding one's own values and how they compare to different cultural contexts.

• Competencies: The skills needed to enter a novel environment, learn it, and be effective. These include both relationship-oriented competencies (like perspective-taking, relationship-building, and humility) and personal self-oriented competencies.

• Experiences: Exposure to different cultural contexts, though Paula emphasizes that experiences alone are not enough.

Paula notes that cultural agility involves the ability to adapt and thrive in unfamiliar cultural settings. She emphasizes that it's not just about giving people experiences abroad, but also equipping them with the knowledge and skills to navigate cultural differences effectively.

Biology is a critical factor in adaptation

Probably the most interesting thing I learned from our conversation was the role hormones play in cultural agility because they can help individuals handle greater levels of novelty comfortably and effectively, and that those with higher cultural agility are often better able to adjust to more challenging cultural contexts.

Did you know that- elevated cortisol levels in response to cultural unfamiliarity can impair cognitive functions, making it challenging to interpret social cues and adapt behaviors appropriately.

Or that

The novelty of a new culture triggers the brain's reward system, releasing dopamine, which can enhance our motivation to engage and learn in the new environment.

I didn’t!

Adaptation begins with undertaking activities that put our chemicals in balance!

Assessment plays a central role in adaptation

I am not going to pass up the opportunity to talk about assessments. Paula has taken what she has learned and created the myGiide assessment, which measures cultural value and cultural agility competencies providing users with insights into their cultural values and biases, allowing for comparative analysis with other cultures and identifying potential areas of conflict or misunderstanding.

The assessment is free. I took it and found the insights it provided me super valuable.

myGiide is also an example of the role technology plays in cross-cultural adjustment

The impact of technology on cultural adaptation may surprise you.

I went into our conversation thinking Paula would gush about how technology has made adapting to other cultures much easier. But I was wrong!

• Technology is a "double-edged sword" for cultural adaptation. It allows people to stay connected to home, potentially reducing feelings of isolation. However, overreliance on home connections can hinder full immersion and engagement with the local environment.

• Technology should not replace real-world experiences and interactions. It should be used as a tool for learning and support. Excessive use of social media and video calls can become a "crutch" that impedes adjustment to the new cultural context. Direct engagement with the host culture remains crucial for successful adaptation because cultural differences are "exacerbated" in virtual environments due to the lack of in-person cues

Businesses must step up to help their expats be successful

When it comes to expat assignments, businesses should:

• Create a pipeline of culturally agile professionals through strategic talent management practices, including the recruitment, selection, and development of employees with the ability to work effectively across cultures.

• Assess bench strength in cultural competencies, not just technical skills, for roles that involve international or multicultural work.

• Use assessments to identify employees who are ready for international assignments or have the potential to develop cultural agility.

• Provide targeted support for employees on international assignments, including in-country cultural coaching and AI-powered tools like the chatbot in the MyGuide platform.

This episode’s Take it or Leave it? articles are:

"Global Mobility in 2024: Trends and Predictions"

• Summary: This vendor-published article outlines the changing landscape of global mobility, including shorter assignments, digital nomads, and the rise of technology platforms for managing mobility. Paula critiques the "nothing new here" approach, emphasizing the need for deeper cultural training.

"Thriving, Not Just Surviving: How Targeted Therapy Makes All the Difference for Expats"

• Summary: This article explores the psychological stress faced by expats and the importance of targeted therapy. Paula reflects on her early research into expat mental health, reinforcing the need for specialized support to help individuals adapt to new cultural environments.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"Hiring is broken not because of a lack of tools, but because we lack a disciplined, strategic approach. Technology only works when we have the right foundation."

–Linda Brenner

• In this episode of Psych Tech @ Work, I welcome my long time friend and collaborator Linda Brenner for some straight talk about the challenges facing TA leaders in the age of talent shortages, AI, and general global insanity.

• This conversation serves as a roadmap for talent acquisition leaders looking to rethink their strategies, streamline their processes, and make smarter use of technology.

• Linda explains why many companies struggle to attract and retain top talent despite using sophisticated AI and other technology solutions.

We delve into the importance of aligning TA strategies with business goals, building clear processes, and minimizing reliance on outdated ATS systems that often hinder rather than help hiring efforts.

We discuss the complexities of AI in recruitment, including video interview assessments and chatbots, and Linda highlights the need for human oversight in areas like candidate engagement and relationship building.

Topics Covered:

Talent Acquisition Audits:

• Linda describes her process for auditing talent acquisition, from evaluating business goals to diving deep into data, processes, and technology use.

• Common issues found in TA audits, including lack of alignment, undefined processes, and inconsistent use of ATS systems.

AI and Video Interviews:

• How AI is currently used in TA and Linda’s views on the limitations and potential pitfalls, particularly around legal considerations and candidate engagement.

Skills-Based Hiring Misconceptions:

• The difference between true skills-based hiring and keyword matching.

• Why many organizations aren’t yet ready to execute skills-based hiring effectively due to foundational issues in their processes and technology.

Takeaways:

Foundation First, Tools Second: AI and advanced tools can’t solve underlying issues. Establishing clear, consistent processes aligned with business goals is essential before adding new technology.

Strategy over tactics: TA leaders should build a strategy that accounts for different types of roles and aligns with company growth goals, instead of relying solely on quick fixes.

Consider the Candidate Experience: Long, inefficient hiring processes lead to drop-offs and high turnover. Streamline processes with candidate engagement in mind.

AI as a Support Tool, Not a Solution: Use AI to support administrative tasks and

data collection but maintain human oversight, especially in high-stakes areas like interviews and candidate assessment.

This epsiode’s "Take it or Leave it" Articles

1. AI-Enabled Work Ethic" by Charles Handler

In this article, I explore whether generative AI is an asset or liability for job candidates and employers. We discuss the ethical considerations around candidates using AI tools in applications and how companies could structure policies to evaluate AI competency fairly.

2. The Future of Talent Acquisition and AI" from Forbes

This article suggests that companies not using AI in talent acquisition will fall behind. Linda and I debate this, with Linda arguing that AI should only be implemented after TA processes are clearly defined and aligned with business objectives.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"We’re generating assessments faster than ever, but our real test is ensuring that these tools are fair and reliable across diverse candidate groups."

–Louis Hickman

In this episode I welcome my friend, super dad, and ex- professional wrestler Louis Hickman for a killer conversation about the ins and outs of using LLMs to create and score assessments.

Louis is a professor at Virginia Tech specializing in research on AI and large language models in assessment and hiring processes. He knows a thing or two about this stuff and we waste no time tackling some really great topics centering around the cutting edge of research and practice on the subject of LLMs and assessments.

This is a must listen episode for anyone developing, or considering developing, LLM based assessments. Or anyone who wants to educate themselves about how LLMs behave when asked to be I/O psychologists.

Topics Covered:

• LLMs in Assessment Center Role-Plays:

• Using LLMs to simulate realistic role-play scenarios for assessments, with the challenge of ensuring consistent, replicable candidate experiences.

• Evaluating Open-Ended Text with LLMs:

• How LLMs score open-ended responses and the observed biases, especially when diversity prompts only partially reduce disparities.

• Consistency in AI Scoring:

• Ensuring LLMs apply scoring criteria consistently across diverse candidates and settings.

• Applicant Reactions to AI Interviews:

• How candidates perceive AI-driven interviews, with many expressing discomfort due to the perceived inability to influence AI decisions compared to human interactions.

• Predicting Responses to Assessment Items:

• The potential for LLMs to predict candidate responses without actual data, though accuracy remains limited by model training and inherent biases.

• Impact on Academic Research:

• LLMs' influence on research publications, with concerns over AI tools favoring self-generated content and potentially amplifying biases in academic discourse.

Listen to the episode to hear the skinny on these topics and more!

And of course we have fun with this episode’s “Take it or Leave it” articles.

Article 1

“The Impact of Generative AI on Labor Market Matching.” An MIT Exploration of Generative AI”,

explores the use of LLMs on matching job seekers and employers.

Article 2

Four Singularities for Research: The Rise of AI is Creating Both Crisis and Opportunity

In this article from Ethan Mollick’s Substack blog One Useful Thing discusses the positive and negative impact of LLMs on academic research.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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Meghan: "Skills are the driver of the future of work. Without validation, there’s no trust, and without trust, you can’t make decisions based on skills."

In this episode of Psych Tech @ Work I welcome my new friend Meghan Raftery who is an Education Designer and skills validation expert who works at Educational Design Lab - a community that is dedicated to doing skills based hiring the right way by ensuring that skills are verified and portable across the world of education and work.

Meghan offers a fresh perspective on skills-based hiring, microcredentialing, and how education and work can align more effectively to prepare students and workers for the future.

Meghan shares her experience transitioning from K-12 education to the world of workforce development and dedicating her efforts to focusing on how to validate skills in a measurable, trustworthy way. She explains how microcredentialing can break down complex skills into smaller, demonstrable pieces that people can stack together over time to build toward larger career goals.

In our conversation Meghan highlights the importance of aligning educational outcomes with workplace needs, particularly through skills validation systems that help employers trust the skills applicants bring to the table. She shares how her team uses human-centered design to create pathways for "STARS" (Skilled Through Alternative Routes) and provides practical insights into how employers can leverage these tools to open doors for candidates who may not have traditional degrees but possess the skills needed for success.

Topics Covered:

• Microcredentialing and Skills Validation:

• Defining microcredentials and how they differ from traditional credentials by breaking down skills into smaller, measurable units.

• The concept of stackable credentials, where individuals can build a portfolio of verified skills over time.

• Human-Centered Design in Education:

• The importance of involving the people closest to the problem in designing solutions for skills validation.

• How Education Design Lab connects learners, educational institutions, and employers to design skills validation systems that work for all stakeholders.

• Skills-Based Hiring and Employer Engagement:

• Challenges employers face in trusting non-traditional credentials.

• How companies can work with organizations like Education Design Lab to ensure they receive reliable, validated skills signals from job applicants.

Takeaways:

• Trust Through Validation: For skills-based hiring to succeed, employers need validated evidence of skills, not just resumes or self-assertions.

• Microcredentials Build Careers: Breaking down skills into smaller, stackable microcredentials allows learners to build toward larger career goals in a personalized way.

• Human-Centered Design: Involving those closest to the problem—whether students, job seekers, or employers—ensures that the solutions developed are relevant and effective.

• Collaboration Is Key: Employers, educators, and governments must collaborate to build systems that bridge the gap between education and the workforce, ensuring skills are verified and trusted.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"The real challenge isn't just defining skills, but creating a system where hiring criteria and performance criteria align—where your hiring approach is integrated into your company’s everyday operations."

—Matthias Schmeisser

Summary:

In this episode of "Psych Tech @ Work," I am joined by Matthias Schmeisser, a talent acquisition leader from Berlin Germany who is passionate about skills based hiring.

Much of our conversation is focused on sharing Matthias’ experience on the path to transforming his company's talent acquisition approach by implementing a skill-based system that ensures alignment between hiring and performance criteria.

Matthias highlights the importance of creating a skill-led organization, where every role is assessed and hired based on well-defined skills rather than previous experience or degrees.

This episode is a must listen for anyone who is interested in executing a skills based hiring process in their organization.

Topics Covered:

• Skills-Based Hiring:

• Moving away from resumes and focusing on validated, functional, and core skills.

• Designing a skill-based career framework that integrates hiring and performance management

• Bias Reduction in Hiring:

• The pitfalls of "pedigree recruiting" and years of experience as a performance proxy.

• How skill-based hiring helps reduce bias and increases the quality of talent.

• The Role of Technology:

• Leveraging tech tools to assess both functional and social skills while enhancing decision-making.

• Using interview intelligence tools to improve interviewer training and ensure consistency in the assessment process.

Takeaways:

• Replace Resumes with Skills: Resumes introduce bias into the hiring process. Focus on specific, validated skills to make more objective hiring decisions.

• Align Hiring and Performance Criteria: Create a consistent framework where the skills used in hiring are the same as those used to assess performance, ensuring seamless integration across the employee lifecycle.

• Use Technology Thoughtfully: Tools like interview intelligence and skill assessments can help make the hiring process more efficient and less biased while providing meaningful data to inform decision-making.

• Start Small: Implement skills-based hiring on a smaller scale before scaling up. Prove the model works by piloting it with key roles and using the results to drive broader adoption.

Articles Discussed in the "Take it or Leave it" Segment:

Matthias and I review two articles about tech and skills based hiring and share our takes.

Article 1: Problems with “Botshit”- what does this mean for recruiting?

• Summary: This article from Business Insider explores the frustrations caused by AI chatbots in customer service. Matthias and Charles discuss the limitations of bots in replicating human interaction and the importance of maintaining quality control when using AI in customer-facing roles.

Article 2: The realities of connecting skills based hiring and education- new infrastructure is needed

• Summary: A Forbes article discussing the disconnect between the education system and the workplace, particularly regarding how skills-based hiring may render traditional degrees less relevant. Matthias and Charles debate the need for both higher education and skills verification, particularly in highly specialized fields like medicine.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"In the absence of trust, people will disengage. No amount of technology or process can replace the human need for belief and belonging."

-Julian Stodd

This episode is a must listen for those looking to expand their ideas about leadership, technology, and the psychology of work!

In this episode of "Psych Tech @ Work," I welcome Julian Stodd, founder of Sea Salt Learning and prolific author whose work challenges conventional views on leadership, work, and technology, by looking at them through new paradigms. Our conversation was refreshing because Julian offered thoughtful reframing of my ideas about psychology in the workplace that are worth noting.

In our conversation Julian reframes the way we think about the evolving workplace, focusing on the shift from traditional hierarchical structures to social leadership and distributed power. His perspectives offer a compelling critique of the conventional social contract between organizations and employees, arguing that this contract has been fundamentally broken by modern work dynamics.

Julian highlights the role of technology, particularly AI, as a tool that both amplifies and disrupts traditional power dynamics within organizations. He also emphasizes the need for organizations to adapt, not just by automating tasks but by fostering trust, belief, and a sense of belonging to truly engage employees. This conversation is a powerful reminder that organizations must evolve beyond transactional relationships and embrace the social elements that drive real engagement and productivity.

Throughout the episode, Julian emphasized the critical balance between formal organizational structures and the informal, social dynamics that truly drive engagement and innovation. He suggested that many organizations over-rely on formal systems—hierarchies, policies, and processes—while underestimating the power of social structures, such as networks of trust, influence, and collaboration.

Julian repeatedly showed me a new way to look at my ideas about the psychology of the workplace. For instance:

The Traditional Leadership Model:

• I Mention: I spoke about the role of leaders in providing clear direction and overseeing teams to ensure productivity.

• Julian’s Reframe: Julian challenged this conventional view, arguing that leadership in the modern era should be about social authority rather than formal hierarchy. He explained that leadership is increasingly granted by the community based on trust and belief, not just a title or position, and that successful leaders must earn this trust by engaging meaningfully with their teams.

The Psychological Contract Between Employers and Employees:

• I Mention: The importance of the traditional idea of the psychological contract as a way to understand the employer-employee relationship, where employees trade their time and skills for compensation and job security.

• Julian’s Reframe: Julian reframed this concept by stating that the traditional social contract has been broken. He argued that organizations need to move beyond seeing employees as resources or commodities. Instead, they should recognize that people are investing their belief and engagement in return for trust, growth, and belonging. Julian emphasized that organizations must rethink this relationship to thrive in the modern, social age.

The Importance of Individual Differences:

• I mention: The importance of the concept of individual differences, focusing on the traditional scientific approach that measures and assesses traits like personality, intelligence, and abilities to predict workplace behaviors and outcomes.

• Julian’s Reframe: Julian suggested a shift away from solely focusing on the science of individual differences as fixed traits to considering individuals as dynamic and adaptable within social systems. He argued that while the science of individual differences is important, it often overlooks the relational and contextual factors that influence behavior. Julian emphasized that people are shaped by their environment, relationships, and the social dynamics they are part of. He proposed that instead of just measuring traits in isolation, organizations should focus on understanding how individuals interact with the system around them and how they can grow and evolve within that system.

"Take it or Leave it": Julian and I give our opinions on articles that suggest a declining need for human workers in the workplace of the future.

• "AI-Powered Companies with No Employees Coming Soon"

• Summary: This article from Sifted discusses the rise of fully autonomous companies that leverage AI agents to run without employees. While the article presents a futuristic view of e-commerce and automation, Julian and Charles critique its narrow focus and overhyped narrative, emphasizing that while AI may automate some functions, people will remain central to innovation and decision-making.

• "The Rise of Autonomous Enterprises"

Summary: This article from HFS explores how data-driven decision-making and automation are shaping the future of organizations. Julian and Charles discuss the potential for organizations to become more efficient through data governance and AI, but caution that organizations still need human judgment and emotional engagement to make meaningful decisions.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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"In the absence of trust, people will disengage. No amount of technology or process can replace the human need for belief and belonging."
-Julian Stodd

This episode is a must listen for those looking to expand their ideas about leadership, technology, and the psychology of work!In this episode of "Psych Tech @ Work,"

I welcome Julian Stodd, founder of Sea Salt Learning and prolific author whose work challenges conventional views on leadership, work, and technology, by looking at them through new paradigms. Our conversation was refreshing because Julian offered thoughtful reframing of my ideas about psychology in the workplace that are worth noting.

In our conversation Julian reframes the way we think about the evolving workplace, focusing on the shift from traditional hierarchical structures to social leadership and distributed power. His perspectives offer a compelling critique of the conventional social contract between organizations and employees, arguing that this contract has been fundamentally broken by modern work dynamics.

Julian highlights the role of technology, particularly AI, as a tool that both amplifies and disrupts traditional power dynamics within organizations. He also emphasizes the need for organizations to adapt, not just by automating tasks but by fostering trust, belief, and a sense of belonging to truly engage employees.

This conversation is a powerful reminder that organizations must evolve beyond transactional relationships and embrace the social elements that drive real engagement and productivity.

Throughout the episode, Julian emphasized the critical balance between formal organizational structures and the informal, social dynamics that truly drive engagement and innovation. He suggested that many organizations over-rely on formal systems—hierarchies, policies, and processes—while underestimating the power of social structures, such as networks of trust, influence, and collaboration.

Julian repeatedly showed me a new way to look at my ideas about the psychology of the workplace.

For instance:

The Traditional Leadership Model:
* I Mention: I spoke about the role of leaders in providing clear direction and overseeing teams to ensure productivity. * Julian’s Reframe: Julian challenged this conventional view, arguing that leadership in the modern era should be about social authority rather than formal hierarchy. He explained that leadership is increasingly granted by the community based on trust and belief, not just a title or position, and that successful leaders must earn this trust by engaging meaningfully with their teams.

The Psychological Contract Between Employers and Employees:
* I Mention: The importance of the traditional idea of the psychological contract as a way to understand the employer-employee relationship, where employees trade their time and skills for compensation and job security. * Julian’s Reframe: Julian reframed this concept by stating that the traditional social contract has been broken. He argued that organizations need to move beyond seeing employees as resources or commodities. Instead, they should recognize that people are investing their belief and engagement in return for trust, growth, and belonging. Julian emphasized that organizations must rethink this relationship to thrive in the modern, social age.

The Importance of Individual Differences:
* I mention: The importance of the concept of individual differences, focusing on the traditional scientific approach that measures and assesses traits like personality, intelligence, and abilities to predict workplace behaviors and outcomes. * Julian’s Reframe: Julian suggested a shift away from solely focusing on the science of individual differences as fixed traits to considering individuals as dynamic and adaptable within social systems. He argued that while the science of individual differences is important, it often overlooks the relational and contextual factors that influence behavior. Julian emphasized that people are shaped by their environment, relationships, and the social dynamics they are part of. He proposed that instead of just measuring traits in isolation, organizations should focus on understanding how individuals interact with the system around them and how they can grow and evolve within that system.

"Take it or Leave it": Julian and I give our opinions on articles that suggest a declining need for human workers in the workplace of the future.
1. "AI-Powered Companies with No Employees Coming Soon" * Summary: This article from Sifted discusses the rise of fully autonomous companies that leverage AI agents to run without employees. While the article presents a futuristic view of e-commerce and automation, Julian and Charles critique its narrow focus and overhyped narrative, emphasizing that while AI may automate some functions, people will remain central to innovation and decision-making. 2. "The Rise of Autonomous Enterprises" * Summary: This article from HFS explores how data-driven decision-making and automation are shaping the future of organizations. Julian and Charles discuss the potential for organizations to become more efficient through data governance and AI, but caution that organizations still need human judgment and emotional engagement to make meaningful decisions.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

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“The challenge isn’t just identifying skills, but ensuring the data is validated, diverse, and reflective of real-world performance."
* Greg Gasperin

Summary: My guest for this episode is Greg Gasperin, CEO and Co-Founder of Merify, a skills data verification platform. Greg and I discuss the future of skills-based hiring, which we both agree requires a shift from traditional resumes and degree-based qualifications to more dynamic and validated assessments of a candidate’s Skills. We discuss the fate of the skills based hiring movement as inexorably bound to the ability to have meaningful, quality evaluation of skills that is based on direct input from humans. Greg discusses the genesis behind Merify based on the need for continuous feedback, data integrity, and community-based validation as essential components of modern talent evaluation. Of course we also also cover the role of AI in automating skill taxonomy updates, ensuring that assessments remain relevant and aligned with industry trends. Topics Covered:
* Skills-Based Hiring: + Moving beyond traditional resumes and degree requirements to more accurate, validated skill assessments. + The importance of diverse, peer-reviewed feedback in creating a trustworthy skills database. * Community-Based Skill Validation: + The role of continuous feedback and real-world performance data in building a dynamic skill assessment system. + How internal talent management can foster trust in skills data before expanding to external hiring. * AI in Skill Taxonomies: + Leveraging AI to maintain up-to-date skill taxonomies and adapt to changing industry demands. + Balancing efficiency with transparency and explainability in AI-driven decision-making.

Takeaways:
* Trust in Data: A reliable skills-based hiring system requires validated, peer-reviewed data that accurately reflects a candidate's real-world abilities. * Continuous Feedback: Regular, diverse feedback is essential for maintaining the accuracy and relevance of skill assessments. * AI for Agility: AI can automate skill taxonomy updates, helping companies stay current with evolving industry needs. * Start with Internal Trust: Focusing on internal talent management builds confidence in the system, paving the way for its broader application in external hiring. * Transparency and Validation: A transparent, explainable system for skill validation is crucial to mitigating biases and fostering trust in AI-driven hiring decisions.

Articles Discussed in the "Take it or Leave it" Segment:
1. "Leveraging Professional Education as a Bridge Between School and Career" * Summary: This article from Fast Company explores the gap between academic learning and practical application in the workplace, emphasizing the need for higher education to incorporate more professional training to better prepare students for their careers. * Discussion: Greg and Charles discuss the importance of exposure to various career paths early in education and the benefits of integrating professional skills training into higher education. * Link: Fast Company Article

1. "To Make the Most of Credentials, We Need a Better Marketplace"
   • Summary: This article from Jobs for the Future highlights the need for a unified marketplace for credentials, where skills and credentials can be transparently evaluated and trusted by employers.
   • Discussion: Greg and Charles talk about the importance of having a trusted, centralized system for credentialing and the role of employer buy-in for the success of a skills-based hiring approach.
   • Link: Jobs for the Future Article

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"It's about selling objectivity, not just science. We need to make sense of the vast data around us to help businesses make better decisions."

Summary:

In this episode of "Psych Tech @ Work," my long time friend Eric Sydell, IO psychologist, genius, and co-founder of Vero AI, joins me to discuss the transformative potential of AI in the workplace and the importance of responsible innovation. This episode offers a deep dive into the practical applications of AI in HR tech, the necessity of ethical guidelines, and how businesses can implement AI responsibly to drive innovation while mitigating risks.

In our conversation we get into the brass tacks of responsible AI.

But first we share some stories about our respective career journeys and life as IO psychologists here in 2024. And how we both find peace and harmony in this crazy world by working with our hands (he builds guitars, I work on old cars).Besides talking about hobbies, my agenda for having Eric on the show was to learn more about what Eric’s company, Vero AI, is doing to help drive safe innovation with AI.

Eric delves into the technical aspects of how Vero AI leverages advanced analytics and AI tools to enhance decision-making processes. He highlights the company's unique approach to converting unstructured data into quantitative insights, enabling businesses to monitor and optimize their operations effectively.Eric emphasizes the importance of continuous output monitoring to ensure AI tools are fair, unbiased, and effective. He explains how Vero AI’s platform uses a combination of AI and rigorous scientific methods to provide comprehensive analyses of algorithmic impact, compliance, and fairness. Our discussion also covers the evolving landscape of AI regulations, the importance of aligning with these regulations, and how Vero AI assists companies in navigating this complex terrain. Eric's insights provide a detailed look at the practical applications of AI in HR tech, underscoring the balance between innovation and ethical responsibility.

Takeaways:
* Sell Objectivity: Focus on using AI to make sense of vast data, helping businesses make better decisions based on rigorous scientific methods. * Monitor AI Outputs: Continuous output monitoring is crucial to ensure AI tools are fair, unbiased, and effective. * Responsible Innovation: Approach AI adoption with a rigorous, ethical mindset. Balance innovation with the responsibility to monitor and understand AI systems. * Regulation Awareness: Stay informed about evolving AI regulations and work towards compliance by maintaining transparent and accountable practices. * Leverage AI Thoughtfully: Use AI to enhance decision-making processes while being mindful of potential biases and ethical considerations.

No show would be complete without the Take it or Leave it” Show.In this episode Eric and I discuss two interesting articles about hiring bias and regulation
Articles Discussed in the "Take it or Leave it" Segment:
1. "Colorado's New Law on Regulating Brain Implants and Neurological Tech" * Summary: This article discusses Colorado’s new law aimed at regulating brain implants and other neurological technologies, focusing on data privacy and ethical concerns. * Discussion: Eric and Charles debate the necessity and timing of such regulations, considering the current state of the technology and the importance of data privacy. 1. "Employers Ask, What is AI? as Regulators Probe Hiring Biases?" * Summary: This article explores the confusion among employers regarding the definition of AI and the importance of evaluating adverse impact in hiring decisions, regardless of the technology used.

Discussion: Eric emphasizes the need for clear definitions and the importance of focusing on outcomes rather than the specific tools used, while Charles discusses the practical implications for employers.

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"It's important to understand that disability is contextual. One can be both enabled and disabled by their neurodivergence depending on the environment and the task at hand."-Nancy Doyle

"The way we diagnose and support neurodivergence needs to evolve. We're still using outdated models that don't consider the full spectrum of human cognitive diversity."-Nancy Doyle

Summary:

In this episode of "Psych Tech @ Work," I welcome my new friend Nancy Doyle, founder and CEO of Genius Within and visiting professor Birkbeck, University of London.In my opinion Nancy is one of the world’s most expert and on-point voices on the topic of Neurodivergence, especially as it relates to the world of work.

It was a real honor to spend an hour with her discussing the complexities and nuances of neurodiversity in the workplace. Nancy brings her extensive experience in IO psychology and coaching to the conversation, offering insights that challenge traditional views and practices around neurodiversity.Nancy shares her journey into the field, highlighting the transition from disability support to specializing in neurodiversity inclusion. Nancy is doing hero’s work emphasizing the need for flexible and inclusive workplace practices that go beyond tokenistic inclusion programs and truly address the functional needs of employees. She also discusses the limitations of current diagnostic practices and the potential for AI and machine learning to aid, but not replace, the nuanced understanding required for effective support.

Topics Covered:
* Early Career Experiences: + Transition from academic learning to practical applications in the workplace. + Differences between academic theories and real-world scenarios. * Practical Applications of Neurodiversity Inclusion: + Importance of creating flexible and inclusive environments tailored to individual needs. + Common workplace accommodations that can enhance productivity and well-being. * Impact of Technology and AI: + The potential and limitations of AI in diagnosing and supporting neurodivergent individuals. + How machine learning and big data can help identify common needs and effective accommodations. * Genius Within: + The amazing work Nancy’s company, Genius Within is doing in providing assessments, coaching, and organizational design services to support neurodivergent individuals, creating inclusive workplaces that enhance productivity and well-being.

Takeaways:
* Understand the Context of Disability: Recognize that neurodivergence can be both enabling and disabling depending on the environment and task. Create flexible workplace policies that accommodate individual needs. * Move Beyond Tokenism: Avoid tokenistic inclusion programs and instead focus on practical, everyday accommodations that support all employees. * Utilize Free Accommodations: Many effective accommodations, such as allowing quiet workspaces or flexible seating, are cost-free and easy to implement. * Leverage Technology Thoughtfully: Use machine learning and big data to identify trends and common needs among neurodivergent employees, but be cautious with AI diagnoses due to embedded biases. * Promote Continuous Learning: Stay informed about the evolving field of neurodiversity and be open to adapting workplace practices to better support neurodivergent employees.

"Take it or Leave it" Articles:In the most fun part of the show Nancy and I discuss two articles and give our opinions on the authors’ takes.
1. "The Danger of Neurodiversity" From the Spectator UK * Summary: This article critiques the neurodiversity movement, arguing that it may dilute the challenges faced by individuals with severe conditions. It highlights the need to balance the celebration of neurodiversity with the recognition of serious disabilities. * Discussion: Nancy acknowledges the validity of the critique but criticizes the article’s tone. She emphasizes the need for a balanced approach that respects all experiences of neurodivergence.

1. "The Rise of Neurodiversity at Work" From Psychology Today
   • Summary: This article discusses how companies are increasingly recognizing the value of neurodiverse employees but warns against tokenistic inclusion programs. It advocates for tailored support that genuinely meets the needs of neurodivergent individuals.
   • Discussion: Nancy agrees with the article’s critique of tokenistic programs and stresses the importance of functional performance and everyday accommodations.

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"It’s important to be a sponge when you're early in your career. Pick up pieces of what everybody else is doing that looks like it's working, and if it doesn't work, then you learn and grow from it." Brandon Sulzberg "Having an open mind early in your career can lead you to discover new interests and opportunities you might have otherwise overlooked." Joe Prinzevalli Summary: In this episode I welcome Brando & Joe, recent graduates from Hofstra University’s IO Psychology program and hosts of one of the most popular IO psychology podcasts- The Brando & Joe Podcast.These guys are the real deal and it is always a great experience to have fellow podcasters on the show. There is a lot we can learn from the collective wisdom they have gained through 80 some odd episodes of their own podcast. Brando & Joe offer me (and my listeners) a fresh perspective on early careers for IOs. We discuss how they found their current jobs and how they are applying what they learned to the world of work. One theme that we spend a good deal of time discussing is the critical role that internships and networking play in shaping career paths, providing us experienced professionals about the importance of giving others a chance.Brandon and Joe’s adaptability, open-mindedness, and continuous learning mindset serve as a powerful reminder of the importance of staying current and flexible in a rapidly changing field. Listen in and you will probably take a relaxing stroll down memory lane and reminisce about how you got where you are today and who helped you get there. Take it or Leave it:The “Take it or Leave it” Show for this episode included a spirited discussion of two super interesting articles:"Will Robots Take My Job?" (website that looks at the future of various jobs)
Summary: This website analyzes the risk of various jobs being automated and the future job prospects for IO psychologists. It highlights the low risk of automation in IO Psychology due to the need for complex problem-solving, creativity, and interpersonal skills. * Discussion: We discuss the implications of automation in IO Psychology, emphasizing the unique human elements that protect their field from being fully automated. * Link:* Will Robots Take My Job

"The Future of IO Psychology: Adapting to AI and Technological Changes" (Linked in Article by Georgi Yankov, Pd.D.)
Summary: Georgi argues that IO psychologists need to embrace AI and technological advancements to remain relevant or it will face extinction. He emphasizes the importance of multidisciplinary approaches and continuous learning. * Discussion: The conversation explores the necessity of integrating AI into IO practices and the potential for rebranding the field to stay current with technological trends. Or else! * Link:* Future of IO Psychology

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"Organizational justice is about ensuring that every individual feels they are treated fairly and with respect in all aspects of their work. It’s not just about the outcomes they receive, but how those outcomes are decided and communicated. Fair processes and respectful treatment are fundamental to maintaining trust and equity within any organization." -Stephen Gilliland

Summary: In this episode of "Psych Tech @ Work," Steven Gilliland, a distinguished professor and expert in organizational justice, joins me to explore the profound impact of fairness on hiring and the psychology of the workplace. Stephen was my professor when I was in grad school at LSU, so I know him well and was lucky to have exposure to organizational justice theory during my most formative years.

After taking a stroll down memory lane, we have an amazing conversation about the fundamental principles of organizational justice theory. We discuss how perceptions of fairness in outcomes, processes, and interpersonal treatment shape employees’ attitudes and behaviors. We talk about how organizations can ensure fair treatment during hiring and how these practices influence applicants’ decisions and organizational reputation. We also dig into the broader implications of fairness in the workplace, emphasizing how companies can navigate challenging decisions, like layoffs, while maintaining their commitment to justice. Finally, we discuss the evolving role of technology in shaping justice perceptions in the workplace. Stephen provides insights into how AI and digital tools are transforming the landscape of organizational justice, offering both opportunities and challenges.

Take Aways:
* Treat Applicants as Customers: Consider the hiring process from the applicant's perspective. Fair treatment during this phase can significantly impact their decision to join your organization and their perception of your brand.

• Understand the Role of Fairness in Employee Engagement: Perceptions of fairness in hiring and workplace practices contribute to overall employee engagement and satisfaction. Ensure that decisions and processes are consistently fair to foster a positive work environment.

• Adapt Organizational Justice to Technological Changes: As workplaces evolve with technological advancements, continuously revisit and adapt your organizational justice practices to address new challenges and maintain fairness.

• Respond to Difficult Situations with Empathy: During tough times, such as layoffs, how you handle the situation reveals your organization's commitment to fairness. Strive to treat affected individuals with empathy and respect, maintaining open and honest communication.

• Align Actions with Psychological Contracts: Be aware of the unspoken agreements between employees and employers. Violating these expectations can lead to perceptions of unfairness and affect employee loyalty and engagement.

• Future-Proof Your Fairness Practices: Stay ahead of emerging trends by integrating fairness into your organizational strategy. Anticipate the impact of new technologies and societal changes on your justice practices to create a resilient and equitable workplace.

Take it or Leave it Show On this week’s show we vote on these articles. Tune in to hear our takes! "ChatGPT and the Rise of AI-Driven Conspiracy Theories" (Source: USA Today)
* Summary: This article explores how advanced AI technologies, like ChatGPT, are fueling the spread of conspiracy theories. It discusses the potential for AI to amplify misinformation and the challenges in managing these effects. The article suggests that as AI becomes more integrated into everyday life, there is an urgent need for radical transparency and robust measures to combat the spread of false information.

“AI Hiring Tools May Be Filtering Out the Best Job Applicants" (Source: CNBC)
* Summary: This article addresses concerns that AI-driven hiring tools may unintentionally filter out qualified candidates due to biases in the algorithms or lack of transparency in decision-making processes. It highlights the growing reliance on AI for managing large volumes of applications and the need for regulations to ensure fairness and accuracy in these systems.

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"Skills-based hiring isn't just removing a degree requirement from a job description. It takes a long time and serious commitment to see it through."
-Matt Alder
Summary:
My guest for this episode is Matt Alder, podcaster, author, consultant and futurist
This is an exciting episode because it includes the first ever episode of The Take it or Leave it Show! This short segment takes place in the middle of our interview. This is a fun segment that provides listeners with expert takes on current hot topics. In the segment, Matt and I review two articles from the current news, share our takes on them, and give a collective thumbs up or thumbs down on it.
This week’s articles cover the topics of skills based hiring and the use of AI driven personality assessments in hiring. Don’t miss it!
In addition to Take it or Leave it, in this episode we learn about Matt’'s background in talent acquisition, his Recruiting Future podcast and his books 'Exceptional Talent' and 'Digital Talent'. Through these efforts and Matt’s work as a top notch consultant and futurist, we discuss the hot topics that are shaping the future of hiring.
Topics covered include:
* The definition of “digital transformation” and its impact on talent acquisition * The role of AI in recruitment and how companies should approach the adoption of new technologies * Challenges in skills-based hiring * The importance of experimentation and innovation in recruitment, and * Predictions for the future of talent acquisition in 2034.

Take Aways:
* Embrace Digital Transformation: Companies must adjust to an increasingly digital world, understanding that digital transformation impacts all aspects of business, including talent acquisition. Digital transformation involves more than just adopting new technologies; it requires a shift in mindset to leverage these tools effectively. * Foster a Future-Focused Mindset: Being open to change and continuously experimenting with new technologies is essential for staying ahead in the HR landscape. Organizations need to think beyond the current processes and explore how technology can fundamentally change their operations.

• Prioritize Skills-Based Hiring: While challenging, skills-based hiring can lead to a more equitable and effective recruitment process, helping organizations find and retain the right talent. This approach emphasizes the importance of assessing candidates based on their actual skills rather than traditional credentials.

• Enhance Candidate Experience: Providing a consumer-grade experience in recruitment processes is crucial for attracting and retaining top talent, particularly among digitally savvy candidates. The goal is to make the recruitment process as engaging and seamless as possible.

• Navigate AI and Automation: Understanding the potential and limitations of AI in recruitment is vital. Organizations should focus on using AI to complement human decision-making and improve efficiency without losing sight of ethical considerations.

Future Hiring Trends: Matt predicts that by 2034, hiring will be highly data-driven, with advanced AI and automation playing a central role. This will lead to more personalized and efficient recruitment processes, but it will also require careful management to ensure fairness and inclusivity

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"From the EEOC's perspective, whether an employment action, employment decision is made by a human or an algorithm, liability is going to be the same for those companies." "AI tools really have the ability to prevent discrimination, but at the same time, they have the ability to discriminate more than any one individual human being." -EEOC Commissioner Kieth Sonderling.
Before we begin- Commissioner Sonderling requested that I share a link to this important report (Algorithms, Artificial Intelligence, and Disability Discrimination in Hiring). While the report focuses on the Americans With Disabilities Act, the ideas put forth apply directly to employment decision making and is an important missive summarizing the government’s position on the relationship between AI and foundational regulation related to concepts such as the ⅘ rule and disparate impact.

Summary:
How lucky are we? My guest for this episode is none other than the grand poobah of employment regulations in the US, EEOC commissioner Keith Sonderling. The Commissioner has many great attributes that underlie his approach to the creation and enforcement of legislation critical to ensuring everyone gets a fair shake when it comes to employment opportunities. But I think one of his greatest attributes is his mission to make himself accessible to all channels of media and communication, including humble podcasters such as myself.
In some sense, my big takeaway from our discussion is the idea that the more things change, the more they stay the same. By this I mean that the central tenets of fair and equitable hiring practices are immutable. While the tools that support employment decision making have, and will continue to become infinitely more complex, ensuring that signals used for hiring decisions are job related, and thus free of systematic differences based on irrelevant factors, is all that matters.
The Commissioner and I have a really awesome and enlightening conversation about the evolving landscape of government regulation on AI in hiring. We begin with a discussion about his career trajectory, his insights about the integration of AI within HR practices, and the critical balance needed between innovation and ethical considerations.
We have fun delving into the specifics of current regulatory frameworks, including the seminal Uniform Guidelines on Employee Selection Procedures and the recent developments in laws such as New York City's Local Law 144. Commissioner Sonderling shares his perspective that the future of regulation will likely be driven by state initiatives rather than new federal legislation.
Takeaways:
* State-Led Initiatives: Commissioner Sonderling highlights that while the federal government may not introduce new legislation soon, states like New York and California are likely to lead the way in regulating AI in hiring. Employers should stay informed about state laws and consider adopting best practices from these regulations proactively. * Navigating a Patchwork Regulatory Environment: With states potentially leading regulatory efforts, HR professionals must prepare to navigate a patchwork of regulations that may vary significantly from one state to another. This emphasizes the need for adaptable compliance strategies. * Existing Federal Standards: Even in the absence of new federal legislation, existing laws and standards, such as the EEOC's Uniform Guidelines on Employee Selection Procedures, still apply to AI-driven employment decisions. Organizations must ensure compliance with these standards to avoid legal pitfalls. * Proactive Compliance through Audits: Commissioner Sonderling advises businesses to conduct regular audits of their AI systems to ensure compliance and prevent discrimination. These audits should be thorough and based on relevant data to identify and mitigate any biases in the system. * Vendor Responsibility and Data Integrity: The discussion highlights the importance of holding vendors accountable for the AI tools they provide. Employers must ensure that their vendors comply with ethical and legal standards and provide necessary data for compliance checks. We can expect vendors to be required to participate in 3rd party audits of their tools at some point in the near future.

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"Everything really starts with the customer. It's not just about having a technology, but understanding the problem you're solving." -George LaRoque Summary: In this episode, George LaRocque, a leading HR technology analyst, investor and the founder of Work Tech- the #1 source of truth around investment and growth in the HR Tech market, joins me to discuss the evolving landscape of HR technology with a twist on its psychological impact on employees and job candidates. We discuss important topics that will define the future of HR tech and ultimately the future of work from the perspective of technology, commerce, and psychology. These topics include:
* Impact of investments on HR tech innovation * Impact of generative ai and automation on work dynamics * The influence of technology on the employee experience * Social and psychological implications of remote work technologies * Adaptation and learning in a tech-driven workplace * Ethical considerations and employee trust

Practical Takeaways:
* Understanding Market Needs: George underscores the importance of aligning technology with actual market needs, emphasizing that successful tech solutions stem from a deep understanding of customer pain points, not just technology's capabilities. He stresses the psychological benefit of technologies that are developed with user-centric designs, which enhance user satisfaction and reduce resistance to new systems. * Impact of Investment Trends on Psychological Well-being: By monitoring where capital is flowing within the HR tech sector, businesses can anticipate and adapt to emerging trends. George's analysis suggests that investments not only indicate which technologies will shape the future of HR but also how these technologies can affect employee well-being. Investments in user-friendly and ethically designed technologies can improve the overall workplace environment by reducing stress and increasing job satisfaction. * Adoption of Generative AI: As AI continues to integrate into HR processes, George advises companies to consider how these tools can enhance operations without replacing the human element. Proper implementation of AI can help reduce employee workload and prevent burnout, but it's crucial to ensure these implementations are ethical and enhance employee experiences rather than making them feel replaceable or surveilled. * Strategic Use of HR Technology for Employee Engagement: The conversation highlights the strategic advantage of using HR technology not just for automation, but for enhancing employee engagement and operational efficiency. Technologies that facilitate meaningful interactions and provide support in workflow can significantly improve psychological comfort and productivity among employees. * Future of HR Tech and Psychological Impacts: Looking ahead, George predicts a shift towards fewer interfaces and more integrated, seamless experiences within HR technology. This evolution is expected to support a less cluttered and more efficient work environment, reducing cognitive load and fostering a more engaging and less stressful work experience. This future of "invisible" technology aids in maintaining a focus on human-centric operations, crucial for mental well-being.

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In this episode I welcome JP Elliot, HR and Talent Executive & Host of the Future of HR Podcast.
JP Elliott is a forward-thinking global human resources executive with extensive experience implementing human capital initiatives that drive business results, improve organizational performance, and elevate company culture.
He is a trusted business partner to C-level and senior management teams who can translate business needs into people strategies aligned with the enterprise priorities and P&L targets.
Over his career, JP has been fortunate to work across a broad spectrum of industries and organizations, many under going significant business and culture transformations. These experiences have shaped him into a versatile, pragmatic, and global leader who builds strong teams that deliver results.
Quote:
“I think we're always looking for shortcuts, but there isn't a shortcut. Most people aren't overnight successes, they're actually twenty year overnight successes. And when you start to hear their journeys and get to know them, you're like, wow, they've had setbacks. They've had these things happen, but they stayed focused on delivering results, understanding the business, building great relationships, and finding a way to differentiate themselves along the way.”
- JP Elliott
Summary:
JP’s podcast is one of my absolute favorite shows. With a constant roster of big time guests, including famous psychologists such as Jeffery Pfeffer and John Boudreau, seasoned HR leaders, and experts on careers and leadership JP’s show has served as a source of inspiration and career development for me. So I am really excited to have him as a guest on my show to talk about his own career and share all that he has learned from his travels as a PhD IO psychologist, HR leader, and podcast host.
I learned so much good stuff from our conversation and I am excited to provide my listeners access to practical, actionable insights about:
* Starting and growing a podcast * Finding your own personalized path to career success through self-development, hard work, and being true to yourself * What it takes to be a good HR leader * Navigating the sometimes competing priorities of thinking and acting like a business person while staying true to psychology and humanistic values. * Using HR leaders’ views on assessments to field a successful assessment program * How to drive impact from the C suite and how to ensure the business notices and appreciates it * How to be smart about the adoption of HR technology

JP’s message of optimism, self-empowerment, and the importance of business acumen and practicality in the game of HR is valuable for anyone, but is especially relevant for IO psychologists with aspirations to navigate their way into HR leadership.
So listen and share liberally!

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Quote:

We talk about the effect of technology on everything, but ultimately, it's the people that matter." This theme has shown up repeatedly, emphasizing that technology doesn't have uniform effects; we have to consider the psychology of implementation, how it's used, and its context​​.

Summary:

In a thought-provoking episode of "People, Science, Inc.," Dr. Charles Handler sits down with Dr. Tara Behrend, a leading expert in the intersection of industrial-organizational psychology and technology. They explore the profound impact of technology, especially AI, on workplace dynamics, employee well-being, and organizational effectiveness. Dr. Behrend, with her extensive background in the study of how technology influences human behavior at work, sheds light on critical issues such as the ethical use of AI in hiring, the psychological effects of remote work technologies, and the future of learning and development in increasingly digital environments.

In this engaging episode of "People, Science, Inc.," Dr. Charles Handler welcomes Dr. Tara Behrend, an esteemed Industrial and Organizational Psychologist and current president of SIOP. Dr. Behrend, also a professor at Michigan State University, shares her insights into the psychological implications of emerging technologies in the workplace, the role of AI in learning and connection, and the significance of career and technical education in addressing evolving workforce demands. With a focus on how AI and technology impact employee well-being, decision-making, and organizational dynamics, this conversation sheds light on the critical intersection of psychology, AI ethics, and workplace innovation.

Take aways:

Balancing AI and Ethics in Hiring: The discussion reveals that while AI can streamline the hiring process, it requires a careful balance to ensure ethical application. Dr. Behrend stresses the importance of transparency and continuous oversight in AI systems to mitigate biases and uphold fairness, providing a blueprint for organizations to follow.

Adapting to Remote Work Technologies: Insights from the episode illustrate that successful remote work depends not just on the technology used but on how it's implemented. Strategies for maintaining communication, fostering collaboration, and sustaining engagement in remote settings are crucial for preserving company culture and employee well-being.
Innovating Learning and Development: Dr. Behrend points out that technology's role in learning and development extends beyond access to information. It involves creating adaptive systems that tailor learning experiences to individual needs, promoting more effective skill acquisition and career growth.

Understanding the Psychological Impact of AI: One of the pivotal learnings is the nuanced psychological impact of AI on employees, including feelings of trust or mistrust towards automated systems. Companies are encouraged to foster an environment where technology serves as a support, not a replacement, enhancing job satisfaction and productivity.
Navigating Technological Change: The episode underlines the necessity for both organizations and employees to remain agile amidst technological advancements. This involves fostering a culture that values upskilling, reskilling, and continuous learning as essential components for thriving in the evolving workplace landscape.

Addressing Surveillance in the Workplace: Through her insights, Dr. Behrend highlights the increasing use of surveillance tools in monitoring employee productivity and behavior. A key learning from her work is the critical need for ethical guidelines and transparent communication about the use and purpose of surveillance technologies. Organizations must balance efficiency and privacy concerns, ensuring that surveillance practices are implemented with respect to employee autonomy and trust, thereby preventing potential negative impacts on morale and workplace culture.

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**"Legislation... has not kept up with the pace of technological advancements, posing significant challenges for ensuring fairness in AI-driven hiring processes."
-Matt Scherer
Summary:
In this episode of "Science 4-Hire," I welcome Matt Scherer, Senior Policy Counsel for Workers' Rights at the Center for Democracy in Technology, a non-profit based in Washington, D.C. The CDT champions the advancement of civil rights in the digital age, striving to ensure technology respects and enhances individuals' rights and democratic values.
Matt and I have an enthusiastic conversation about the importance of understanding and navigating the evolving landscape of AI and automation in hiring processes.
Matt brings his expertise to the table, dissecting the intersection of emerging technologies with workplace rights, the nuances of AI legislation, and the vital role of public policy in safeguarding fairness and privacy.
Matt and I dive right into some great dialogue about the challenges posed by electronic surveillance, automated management systems, and the quest to elevate worker voices through technology.
We spend a good deal of time focusing on the critical evaluation of AI hiring tools, highlighting New York City's Local Law 144 and its implications for a broader regulatory framework.
Matt provides some really interesting and important points about the criticality of using a design-first mentality in developing AI tools as a critical part of ensuring they serve to enhance worker empowerment rather than diminish it.

Insightful Moments:*** Matt discusses the Center for Democracy in Technology's (CDT) mission to advance civil rights in the digital age, focusing on the workplace implications of emerging technologies such as AI and automated management systems. The CDT's commitment to this cause is grounded in ensuring technology serves to enhance, not undermine, workers' rights and privacy.

• The conversation highlights New York City's Local Law 144, examining its strengths and weaknesses in regulating AI hiring tools. Despite being a pioneering piece of legislation, Matt suggests that the law is riddled with loopholes that many companies exploit to avoid compliance, demonstrating the challenges in crafting effective regulatory frameworks.

• Matt emphasizes the importance of design-first thinking in developing AI technologies for hiring. He argues that most challenges associated with AI and automated hiring tools stem from design issues, advocating for a holistic approach that integrates ethical considerations from the outset.

• The dialogue touches on the role of transparency in AI-driven hiring processes. Current practices often leave candidates in the dark about when and how they are being evaluated by AI, stressing the need for legislation that mandates clear disclosure to candidates.

• An exploration of upcoming legislation reveals a split between stronger regulatory regimes advocated by civil rights groups and more loophole-ridden proposals pushed by tech companies. This tension underscores the ongoing debate over how to effectively govern AI in hiring while protecting workers' rights.

• Matt shares insights into the civil rights standards for 21st-century employment selection procedures, a document aimed at modernizing and expanding upon the 50-year-old uniform guidelines for employee selection procedures. This initiative reflects a broader effort to update legal and ethical standards for employment assessments in light of advancements in AI and technology.

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In this episode of Science 4-Hire, I welcome my new friend Martyn Redstone, founder of pplbots and a pioneering force in the intersection of conversational AI and recruitment technology.

We have a fun and meaningful conversation about the evolution of chatbots and the role of generative AI in the recruitment process. With over two decades in technology and a laser focus on conversational AI solutions for recruitment, Martyn shares his journey through the advancement of chatbots from simplistic decision trees to complex systems empowered by natural language understanding, processing, and now, generative AI.

We thoroughly explore the implications of chatbots and AI technology on candidate experience, the nuances of designing effective chatbot interactions, and the potential pitfalls and promises of leveraging large language models in recruitment.

This conversation delves into the significant shifts in recruitment technologies, the criticality of design-first approaches, and the careful balance between innovation and ethical considerations in implementing AI tools.

Quotes:

"Now, we're seeing the move to generative AI-based chatbots... but it always comes down to having a design-first mentality."
-Martyn Redstone:

Insightful Moments:

Evolution of Chatbots: Martyn illustrates the journey from basic chatbots to sophisticated systems enhanced by conversational AI and generative AI technologies, highlighting the transformative impact on the recruitment landscape.

Design Challenges: The conversation illuminates the complexities behind designing chatbot experiences that are not only technologically advanced but also ethically sound and user-friendly.

Generative AI in Recruitment: They discuss the advent of generative AI in recruitment, addressing both its potential to revolutionize candidate engagement and the inherent risks of relying too heavily on such models without adequate safeguards.

Practical Applications: Martyn shares insights into real-world applications of conversational AI in recruitment, from enhancing candidate screening to re-engaging talent pools through intelligent, automated interactions.

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"This AI piece and all the ethics and governance and everything that goes around that... it really warrants a dedicated role and some specific communities focused on AI ethics and risks."
Bob Pulver: Founder of Cognitive Path

Summary:

My guest for this episode is Bob Pulver, a seasoned expert in the intersection of artificial intelligence and talent acquisition, bringing with him over two decades of experience from his tenure at IBM to the forefront of AI ethics and responsible implementation.

This episode not only provides valuable insights into the mechanics of implementing
responsible AI, but also frames a narrative that reveals the complexity and necessity of ethical AI practices in today's technology-driven hiring landscape.

Bob underscores the importance of ethical AI development, emphasizing responsibility by design, speaking to the need for a proactive stance in integrating AI into people practices. We both agree that compliance should not be a band-aid, or afterthought, but a foundational principle that begins with data acquisition and continues through to the implementation of AI-powered tools.

A big part of our conversation revolves around legislation related to the use of AI hiring tools, including New York City's Local Law 144. Bob provides advice to organizations on navigating its anti-bias legislation and the broader implications for global regulatory landscapes.

In sum, Bob and I both agree that responsible AI is not a game of short sighted interventions, but rather a transformative shift that affects every aspect of talent acquisition. We provide our ideas on how to navigate through this period of intense change, focusing on the practical challenges companies face, from internal upskilling to grappling with legislation that struggles to keep pace with technological advancements.

Takeaways:
* Start with a Foundation of Ethics and Responsibility: Implementing responsible AI requires building your technology on a foundation of ethical considerations. This involves considering the impact on protected groups, ensuring accessibility, and integrating privacy and cybersecurity measures from the beginning. * Understand and Comply with Relevant Legislation: Staying informed about and compliant with anti-bias legislation, like New York City's Local Law 144, is crucial. This law requires annual independent audits for automated employment decision tools, ensuring they don't adversely impact protected classes. * Adopt a Holistic Approach to AI Implementation: Responsible AI transcends legal compliance to include a broader ethical framework. It encompasses fairness, privacy, cybersecurity, and the mitigation of various risks, including reputational, financial, and legal. * Engage in Continuous Education and Upskilling: All stakeholders, regardless of their role, need to be educated about the ethical implications of AI. This includes understanding how to acquire and test data to mitigate bias and ensure the responsible use of AI technologies. * Foster a Multi-Stakeholder, Cross-Disciplinary Dialogue: Creating solutions that are both innovative and responsible requires input from a diverse group of stakeholders. This includes technical experts, ethicists, legal teams, and end-users to ensure cognitive diversity and address the ethical, cultural, and practical aspects of AI.

Prepare for an AI-Driven Transformation: Recognizing that AI transformation affects every aspect of an organization is essential. This realization should drive a commitment to responsible AI practices throughout the organization, from product development to deployment.

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“So most of the world is made up of full stack application developers who build software for anything from HRIS to accounting, to supply chain, and what have you? For those people to easily add generative AI capabilities into their applications while remaining in compliance with the security, trust, and safety requirements that enterprises have, well that's a fairly difficult challenge.” Vivek Sriram- Co-founder of Bookend.ai

Summary: In this episode of Science 4-Hire, I welcome my old friend and partner in crime Vivek Sriram, co-founder of Bookend AI, a start up that provides secure infrastructure that supports the efficient spin up and fine tuning of open source LLMs.

We waste no time delving into the fascinating, confusing, and intricate world of Large Language Models (LLMs) and their burgeoning role within enterprise solutions, with a special focus on HR and hiring applications. We sure do agree on the transformative potential of LLMs to revolutionize enterprise software, enhancing functionalities such as candidate screening, resume parsing, and even generating interview questions—tasks pivotal to modern HR departments.

Vivek brings me back down to earth a bit as he provides words of caution about the considerable challenges that come with integrating LLMs into enterprise systems, especially within the HR sector. Concerns around data privacy, the risk of perpetuating biases, and maintaining compliance with labor laws are significant when deploying AI in hiring. Vivek emphasizes the critical need for enterprises to navigate these challenges carefully, ensuring that LLM integration respects ethical guidelines and regulatory requirements, thus preventing potential adverse impacts on candidates and the hiring process.

The good news is that Vivek outlines strategies for implementing LLMs in a manner that balances innovation with responsibility. Approaches such as utilizing open-source models for greater control and customization, and employing platforms that offer secure, compliant AI integration, are discussed as viable solutions. The idea of fine-tuning LLMs with proprietary data to better align with specific HR needs provides additional levels of confidence for those looking to use LLMs securely.

Listeners will gain a nuanced understanding of the dual-edged nature of LLMs in HR and hiring contexts—recognizing their potential to significantly improve efficiency and decision-making in talent acquisition, while also grappling with the ethical, privacy, and compliance issues inherent in their use. This episode underscores the importance of thoughtful AI integration in HR practices, aiming for a future where technology serves to augment human judgment rather than supplant it.

Take Aways:
We must recognize that integrating open-source LLMs into enterprise applications comes with complex challenges, including navigating licensing, data usage permissions, output control, and auditing requirements. * Enterprises must address strict compliance and security standards, especially in regulated industries, when implementing open-source LLMs. This includes ensuring data privacy, adhering to industry-specific regulations, and maintaining the integrity of sensitive information. * Tailoring open-source LLMs to specific enterprise needs requires significant customization. Enterprises need to modify these models to align with their unique operational, compliance, and ethical standards. * Potential solutions to the issues inherent with the use of LLMs involve employing strategies for effective management of open-source LLMs, include: * Selective Model Adoption: Carefully selecting open-source models that best fit the enterprise's technical and compliance requirements. * Data Management and Security: Implementing robust data management practices to ensure that the use of LLMs complies with data privacy laws and enterprise security policies. * Model Customization and Fine-Tuning:* Customizing and fine-tuning open-source LLMs with enterprise-specific data to improve relevance and performance while adhering to ethical guidelines. * It is essential to stay informed about evolving regulatory landscapes related to AI and machine learning technologies to ensure ongoing compliance and adapt strategies as needed.

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"There is no one way to do recruitment right, there are many ways to do it right but even more ways to do it badly." -Bas van de Haterd

"The reality is that we're in an era of experimentation. We have to experiment continuously." -Kevin Wheeler Summary:

In this episode of Science 4-Hire I welcome two old friends, distinguished Science 4-Hire alumni, and talent acquisition experts, Kevin Wheeler and Bas van de Hatred.

The show is dedicated to a deep dive into their new book "Talent Acquisition Excellence,", which is a practical handbook for those who wish to learn how to apply best practices using the holistic framework created by these two experts as their guide.In our discussion we explore what excellence in talent acquisition actually looks like. In doing so, we delve into the book's key themes, including the transformative role of artificial intelligence in hiring, the necessity for ethical frameworks in AI deployment, and the importance of adapting talent acquisition strategies to fit organizational culture.

One of the most important themes from our conversation is the necessity for talent acquisition professionals to adapt and evolve, emphasizing the need for experimentation and a strategic approach to integrating AI into talent acquisition processes. Kevin and Bas also share their journey of collaboration during the pandemic, highlighting how their perspectives from different corners of the globe have influenced the book's content. The conversation also addresses the challenges of navigating a landscape where technology outpaces policy, underscoring the need for talent leaders to innovate responsibly.Key Takeaways:
* Multifaceted Approaches to Talent Acquisition: The book emphasizes that there's no one-size-fits-all strategy for recruitment; success lies in customizing approaches to fit organizational culture. * The Critical Role of AI in Hiring: Insights into how AI is reshaping the efficiency and scope of assessments, with a look towards future technologies like interactive holograms. * Ethical Use of AI: The discussion sheds light on the ethical implications of AI in hiring, advocating for transparency and human oversight. * Adapting to Technological Advances: The authors stress the importance of talent leaders being open to experimentation and adaptation in the face of rapid technological changes. * Policy Development on AI Usage: A crucial takeaway is the immediate need for organizations to develop policies regarding candidates' use of AI in applications.

Listeners will gain not only a nuanced understanding of the current state and future directions of talent acquisition but also practical advice on navigating these changes. This episode is a must-listen for anyone looking to stay at the forefront of hiring practices in the AI era while staying true to the foundations and frameworks required to do talent acquisition the right way.

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“I really see the technology as a driver of user friendliness of assessment in the future and because people don't have time anymore to sit down for many hours and do writing samples and take personality tests of 300-500 questions.”
-Georgi Yankov

Summary:

In this forward-looking episode of Science 4-Hire, I welcome Georgi Jankov, a fellow IO psychologist and futurist. We take a dive deep into the innovative intersections of AI with psychometric measurement and managerial/executive assessment.

The episode focuses on how AI and machine learning are currently being applied to revamp traditional assessment processes, predicting significant shifts in the landscape of talent evaluation and development.

Georgi and I discuss the transformative potential of AI in making assessments more efficient, personalized, and scalable, highlighting the move towards more interactive and immersive methods, such as the use of holograms for role-plays and simulations in assessment centers.

The conversation also addresses the ethical dimensions of integrating AI into human-centric processes, stressing the importance of balancing technological advances with the intrinsic value of human judgment and empathy. We discuss how we both stand firm in our convictions about the necessity for IO psychologists to adapt and collaborate with AI. Agreeing that, to enhance the quality and reach of assessments, we cannot compromise the personal touch that is crucial to understanding human behavior and potential.

Key Takeaways:
* Revolutionizing Assessments with AI: AI and machine learning are set to transform the efficiency and scope of psychological assessments, making them more adaptable and insightful.

• Interactive and Immersive Techniques: Future assessments may utilize holograms and virtual reality, providing richer, more contextual evaluations of candidates.

• Ethical and Human-Centric Approach: Despite technological advancements, maintaining ethical standards and human empathy remains paramount in assessment processes.

• Collaboration Between Psychologists and AI: The future of assessments lies in the synergistic collaboration between IO psychologists and AI technologies, leveraging the strengths of each to enhance talent identification and development.

• Predictive Analytics and Personalization: AI's ability to analyze vast datasets will enable more personalized feedback and developmental insights, tailoring the assessment process to individual needs and potentials.

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Mary Ellen Slayter: "AI is a race to the middle. Generative AI content is, by my definition, mediocre."

In this episode I take a bit of a departure from the world of hiring to talk with my long time friend, Mary Ellen Slayter, the founder of Reputation Capital, a top notch digital content marketing firm.

Don’t be afraid of the fact that we do not talk about hiring. In this day and age creating great content is a critical skill for any professional, so don’t miss this opportunity to learn from the best!

Mary Ellen and I have a ton of fun exploring the evolving landscape of content marketing in the age of AI. Mary Ellen, with her rich background in journalism and content marketing, delves into how AI tools like ChatGPT are impacting content creation and thought leadership. Mary Ellen discusses the shift in content marketing strategies, the role of AI in generating and refining content, and the challenges of maintaining originality and quality in an AI-saturated environment.

The conversation also touches upon the importance of human creativity and critical thinking in the face of AI-generated content. Mary Ellen emphasizes the need for thought leadership that provokes public debate and reflection. The discussion further explores the use of AI in various stages of content creation, from ideation to final output, and how professionals can leverage AI without losing their unique voice and perspective.

Key Takeaways:

AI's impact on content creation and the challenge of maintaining originality.The importance of human oversight in AI-generated content to ensure quality.Strategies for effective thought leadership in an AI-dominated landscape.

Practical tips for using AI tools in content marketing without compromising uniqueness.The evolving role of content creators and marketers in the era of AI and automation.

Listeners will gain valuable insights into the integration of AI in content marketing, strategies for maintaining authenticity, and the future of thought leadership in the digital age.

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"Personnel selection is shaking on its foundations. Lots of the assessments that we have, that we do, suddenly we're not quite sure if they're still as effective.” “I feel as if now it's starting to become the responsibility of organizations who are hiring to make sure that people are using it (Chat GPT) in the same way so that that they can clearly state, “This is how you can use Chat GPT or cannot use Chat GPT” -Djurre Holtrop “From what I've seen, not many talent acquisition teams that I've encountered have really thought carefully about what they what message they wanna send to candidates about the use of technology. And I I think one one concern I do have is if the reaction to it is, “Oh, we've gotta monitor everything.” I just worry about the candidate experience impact that will have.” Patrick Dunlop Summary:In this episode I host Patrick Dunlop from Curtin University in Perth, Australia, and Djurre Holtrop from Tilburg University in the Netherlands, discussing the evolving role of AI in hiring and personnel selection and their research into the use of ChatGPT by job applicants.We traverse across three continents, bringing together two interesting foods, Vegemite and Stroopwafles as well as global insights on the use of AI in the hiring process. Together myself, Dunlop, an organizational psychologist, and Hultra, an assistant professor with a background in assessment psychology, delve into the practical and ethical implications of using large language models like GPT in job applications and recruitment.The conversation explores how AI is reshaping traditional hiring processes, with a focus on my guest’s research into the use of AI in enhancing cover letters and the potential biases and ethical considerations that emerge. The guests share insights from their ongoing research, including the impact of AI on candidates whose primary language is not English and how organizations might need to adapt their selection processes in response to technologies such as Chat GPT.. The episode also touches upon the future of AI in hiring, contemplating the balance between technological advancements and maintaining fairness and inclusivity in recruitment. Key Takeaways:
* AI is significantly changing organizational selection systems, particularly in enhancing application materials like cover letters. * Research into the use of LLMs in the hiring process is critical, but is difficult because of how fast things are moving. * We can all agree that LLMs and GPT are going to create seismic shifts in hiring, but the “how remains unknown” * Insights from different continents show a diverse understanding of AI's role in hiring and its broader implications.

So listen in and you will gain a comprehensive understanding of the impact of AI on global hiring practices, along with practical insights and future prospects in the field of AI and personnel selection.

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“When GPT 3.5 came out. I was like, oh my gosh, we don't need to train the AI anymore! We just need to prime it. It's so similar to psychology! Instead of programming and language, we'd do it in English.”

In this episode of Science 4-Hire, I chew the digital fat with Dr. Matt Barney, founder and CEO of XLNC, a company that is using AI to create the future of leadership coaching.

Dr. Barney and I laud the transformative role of AI in talent development and assessment. Dr. Barney, with his profound expertise in IO psychology, AI, and leadership development shares insights from his journey in integrating these domains. Dr. Barney and I share the same passion for “distributed:” interventions such as assessment and coaching. By distributive I mean the on-demand, and on-going delivery of relevant information for measuring and improving performance. We talk about how, with this modality, the world we live in becomes a measurement and feedback session, liberating people practices from the confines of any one specific location or situation.

The discussion covers the evolution of AI from a tool requiring extensive training to one that can be effectively primed for diverse applications using plain old human speech or writing. We look at this phenomenon through the lens of leadership development and coaching.

Dr. Barney articulates the challenges and breakthroughs in AI, particularly focusing on generative AI's impact on talent development and coaching. He describes his innovative approaches, like his LeaderAmp platform and his recently created “CialdiniBot”, in which he has created a digital persona of renowned social psychologist and master in the science of persuasion, Robert Caildini. The CialdiniBot is a living example of the possibilities that are available when we blend psychological insights with AI for more effective and powerful leadership and persuasion training at scale.

The episode goes beyond technical discussions, delving into the humanistic and ethical considerations of AI application in talent development. Dr. Barney's unique blend of IO psychology expertise and AI innovation offers listeners a glimpse into the future of talent development and assessment, where AI not only enhances efficiency but also ensures ethical and human-centric approaches.

Key Takeaways:
* Listeners will gain a comprehensive understanding of the current state and future possibilities of AI in talent development, along with practical insights into the integration of AI with IO psychology principles. * Specifically how to use LLMs that integrate AI with IO psychology to create effective leadership training and assessment at scale via coaching personas. * The importance of trustworthy and explainable AI in ethical talent development practices. * Future prospects of AI in transforming talent assessment and development processes- including how LLMs are making the future possible.

Follow Dr. Barney by visiting his website XLNC.co or by finding him on LinkedIn

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"As a journalist, I've seen the many facets of AI in hiring – it's not just about the technology, but about the people it affects." - Hilke Schellman

In this revealing episode of Science 4-Hire, Dr. Charles Handler welcomes Hilke Schulman for a captivating conversation on AI in hiring processes and its toll on the rights and psyche of job seekers.

As an investigative journalist, Hilke has been on a six year long quest to understand how AI hiring tools are being used, their credibility and their impact on job seekers. This episode delves into her experiences and insights that culminated in her recent book (The Algorithm), an excellent expose that sheds light on the complexities and ethical challenges of AI in hiring.

One of the great things about this episode is that Hilke brings a fresh, inquisitive outsider's perspective to the table. In the process of writing her book, Hilke has interviewed hundreds of people on all sides of the AI hiring game and has taken a great many AI based assessments herself. On the show we discuss the ethical dimensions of technology, and delve into the nuanced challenges and ethical dilemmas presented by AI in hiring practices and a range of related topics such as the pitfalls of AI tools, the industry behind the creation of these tools, the impact of these technologies on protected classes, especially those with disabilities, and the need for greater transparency and ethical consideration in the industry of AI hiring tools.

Highlights:

Ethical Challenges of AI in Hiring: Schulman highlights the ethical challenges and potential biases in AI tools used for hiring, emphasizing the need for critical scrutiny of these technologies.

Impact on Diverse Groups: The conversation explores the disproportionate impact of AI hiring tools on different groups, particularly those with disabilities, showcasing the need for inclusive and fair hiring practices.

Advocacy for Transparency and Ethics: Schulman advocates for greater transparency in AI technologies and ethical considerations in their deployment, urging companies to be more accountable.

Future Prospects and Concerns: The episode discusses the future of AI in hiring, considering both its potential benefits and the risks it poses, especially regarding privacy and discrimination.Do not miss Hilke’s book: "The Algorithm: How AI Decides Who Gets Hired, Monitored, Promoted, and Fired and Why We Need to Fight Back Now” which is now available everywhere you can get a book! Hilke can be reached at: hilke.schellmann@gmail.com and via Linkedin.

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“When you're a linguist, especially a syntactician, when you look at language, it's kind of different from how other people look at language. You kind of see the structures under it, kind of have this x-ray vision, right, of of what's going on under the hood.”
- Karin Golde, Founder of West Valley AI.

In this awesome and inspiring episode of Science 4-Hire, Dr. Charles Handler engages in an enlightening conversation with Karin Golde, Ph.D, founder of West Valley AI and a Silicon Valley AI veteran.

Karin does an amazing job of laying down a solid foundation for the conversation with this truism:

“I think is one of the most tricky parts about AI is dealing with language data.”

From here Karin, with a background in linguistics and extensive experience in leading AI data teams, delves into the evolution of AI, its implications, and ethical considerations in data sourcing and model training.

From this foundation the discussion orbits around the intriguing intersection of artificial intelligence and industrial-organizational psychology, particularly focusing on high-tech use cases related to AI and hiring.

Key Takeaways:

AI Evolution and Language Data: Karin illustrates her journey from studying theoretical syntax and semantics to leading AI data teams. She emphasizes the tricky parts of dealing with language data in AI and how the evolution of AI has shifted from understanding the structure of language to more sophisticated generative AI models.

Ethical Considerations in AI: A significant part of the discussion revolves around the ethical concerns related to the sourcing and labeling of data for AI. Karin sheds light on the often overlooked human labor aspect, highlighting issues like low-wage labor, the quality of data, and the potential psychological impact on workers involved in tasks like toxicity classification.

AI and IO Psychology Intersection: The conversation explores how AI can be leveraged in IO psychology, particularly in deriving insights from text and evaluating qualitative data. Karin suggests the potential of AI to scale and refine the process, though with an emphasis on the need for expert oversight to ensure quality and ethics.

Future of AI in Hiring and Talent Assessment: The episode touches upon the future possibilities and challenges of integrating AI more deeply into hiring processes and talent assessments. Discussions include the potential for creating more efficient systems while also acknowledging the need for robust ethical frameworks and data quality standards.

Visit West Valley AI's website westvalley.ai to learn more about Karin Golde's work.
Reach out to Karin on LinkedIn or via email at karin@westvalley.ai for wisdom on AI applications in HR tech or people analytics.

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"We bring data and insights right from that data to our decision-makers... that's how we make people smarter about people." - Christina Norris Watts

This episode offers a unique perspective on balancing innovation with ethics in the rapidly changing world of HR technology.

My co-pilot for this journey is Christina Norris Watts, Head of Assessment and People Practices Johnson & Johnson.

We start the episode with some serendipity, both relaying that Band aids are our favorite J&J product. After sharing some fun stories, we dive deeply into the evolving landscape of talent assessment in HR, and the amazing work Chiristina has done to help create a best in class assessment center of excellence.

We focus on the role of scientific methods in safely and ethically exploring the new frontiers of AI in the workplace. Of course no podcast these days would be complete without some discussion of large language models. We got that covered, as we wax poetic on how LLMs are revolutionizing HR practices. But wait, there’s more! Christina and I discuss the importance of data-driven decision-making, the ethical implications of AI in HR, and the future of talent management.

Key Takeaways:

Integration of AI in HR: The episode highlights the growing importance of integrating AI and large language models in HR processes for better decision-making.

Ethical Considerations: Christina discusses the ethical aspects of using AI in HR, emphasizing transparency and fairness, noting their importance as a guiding star.

Importance of Data-Driven HR: The conversation underscores the need for HR to be data-driven, using insights from data to inform decisions about talent management.

Challenges in HR Innovations: The episode addresses practical challenges in implementing new technologies in HR, including system integration and maintaining the validity of assessments.

Future of HR Technology: Christina shares thoughts on the future of HR technology, particularly the potential shift towards more passive assessment methods.

Role of HR in Innovation: The importance of HR's involvement in technological innovations within a company is discussed, particularly in terms of ethical considerations and understanding job changes. This episode offers a comprehensive look into the future of HR technology and the critical role of data and ethics in shaping HR practices.

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People analytics is not just for psychologists anymore!

"I think that not only will people analytics be the decision maker themselves. In the future, I actually think people analytics will be what HR is."
–Cole Napper on People Analytics

My guest this week is Cole Napper, IO psychologist extraordinaire, People analytics thought leader, and inspirational podcast host.

In this fun filled episode I take a page from Cole’s playbook and loosen my collar, clearing the air for super engaging personal stories interwoven with thought leadership about creating a new era for people analytics.

We discuss a range of topics, including Cole's background from Louisiana, his experience at Louisiana Tech's IO Psychology PhD program, and his role as VP of People Analytics at Orgnostic.. Cole is also a co-host of the Direction Correct podcast about People Analytics and is dedicated to creating a future where people analytics and technology combine to have a positive impact on business outcomes and happiness at work..

The episode covers various aspects of people analytics, its role in business, and its intersection with technology and AI. Additionally, they touch upon current issues like COVID-19, its impact on the workplace, and the future of people analytics in HR.

Topics Discussed:

Introduction to Cole Napper: Cole's Louisiana background, education at Louisiana Tech's IO Psychology PhD program, and his current role.

People Analytics: Insights into what people analytics entails, its significance in the business world, and how it's shaping the future of work.

Impact of COVID-19: How the pandemic has affected workplace dynamics and the role of people analytics in navigating these changes.

The Role of Technology: Discussion on the intersection of people analytics with AI and machine learning.

Challenges and Future Trends: Exploring current challenges in the field of people analytics and predictions for its future trajectory within HR.

Key Takeaways:
* Podcasts can be fun and still pack quite a bit of learning into the mix. * People analytics is a vital component in understanding and improving organizational dynamics. * The COVID-19 pandemic has accelerated the need for robust people analytics. * Technology and AI are becoming integral in advancing the field of people analytics. * There are ongoing challenges in data privacy and quality in the use of AI and analytics tools.

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We are all in this together! Don’t miss the chance to expand your mind with a global perspective on issues that will help define the future of global policy work, commerce, and life in general.

In this intriguing episode of "Science 4-Hire," I welcome Arnoud Engelfriet from the Netherlands to discuss AI ethics and governance, particularly in the context of the European Union and his new book on AI compliance. Engelfriet brings a wealth of experience in computer science and law, offering deep insights into the regulatory landscape of AI and its broader implications. The interdisciplinary, global perspective that Arnoud brings to the table is an essential part of the future of ethical AI and AI compliance.
Key Discussion Points:

AI and Algorithm Legal Compliance: Engelfriet talks about his upcoming book "AI and Algorithms: Mastering Legal Compliance," which details the European approach to AI regulation and AI compliance, focusing on managing risks associated with AI and algorithms​​.

You can pre-order the book here.

AI Act and Ethical Considerations: The conversation delves into the European AI Act, its genesis post-Cambridge Analytica, and its focus on fundamental human rights. Engelfriet emphasizes the need for AI to be ethical and lawful simultaneously​​.

Challenges in AI Implementation: Arnoud and I discuss the practical challenges in AI applications, particularly in hiring and fraud detection. They highlight the difficulties in creating unbiased AI systems and the importance of accountability in AI deployment​​.

The Role of AI Compliance Officers: The rise of the AI compliance officer role is noted as crucial for organizations to adhere to evolving AI regulations and ethical standards​​.

Auditing AI Tools: Arnoud shares his experience in auditing predictive hiring tools, emphasizing the importance of transparency and the challenges in ensuring bias-free AI​​.

Global Impact of AI Governance: The episode concludes with reflections on the global relevance of AI governance and the need for organizations to proactively align with ethical standards and regulations​​.

Notable Quotes:
"At some point, you're gonna have to trust the people that made the AI system." - Arnold Engelfriet on the challenges of fully understanding AI decisions​​.

"It's incumbent on organizations using these tools to do their own studies locally to see what's happening." - Dr. Charles Handler on the need for organizations to police their AI tools​​.

Call to Action:

Listeners are encouraged to explore more about AI ethics and governance and how these evolving standards impact global business practices. Order Arnoud’s book now, visit Rocket-Hire.com and check out Ethics-Hire Prism framework and download our FAQs document on New York City local law 144.

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Full disclosure: This episode was recorded before our break. In the normal world, not much would have changed in 5 months. But in the new reality of Generative AI- changes have occurred since we recorded this episode. They do not make the research discussed any less valuable, because the trends were identified- but the execution of them is ever evolving.
More full disclosure: There are two sets of show notes presented below, one created by me and one created by Chat GPT. I have no shame in using GPT to help me with the show notes. It is super interesting to show what I came up with vs. what it came up with.
My notes:
In this episode we welcome Stacia Sherman Garr co-founder of Red Thread Research.
In our discussion Stacia and I accomplish two things:
We share our perspectives on doing quality vendor/market research, evaluation and classification and doing it well
Stacia relays the results of her research into the current state of the people analytics software technology market and the vendors who define it.
Our discussion begins with level setting to define exactly what people analytics software technology is, and is not. This includes the reality that this technology is not really focused directly on talent acquisition. Stacia defines people analytics technology as follows:
"People analytics is the collection, understanding, and eventually application of people data. It's designed to improve what's happening with the people themselves and to improve business outcomes. So it's really all data that has to do something with people and with an intention of making people's lives better and making the business better"​​.
Our discussion into research delves into how to do good research into a specific market, looking both at the vendors themselves and the macro trends. We acknowledge how difficult it is, but also how important it is to those who provide and consume the technology.
Finally, Stacia reveals the major trends in the market and the impact of Covid and advances in generative have had and how they are shaping the future of the market. Covid changed things by introducing mass scale remote work and the increased need for monitoring analytics. AI has created so much change so fast, that Stacia has created an entirely new study to track it.
Trends identified include:
The size of the people analytics software market has grown rapidly- but is leveling off
Prices are dropping
There is a push to more of a SaaS model where implementation and services are handled by 3rd party consulting firms.
There is generally lower satisfaction amongst users of the products. Part of this is because the use cases have expanded but the products were not prepared for this.
AI demands entirely new levels of support
Multi-source people analytics software platforms are the biggest new trend. For instance surveillance and wearables are becoming more in focus- and these can create major trust issues amongst employees.
Here is what GPT came up with when I fed it the transcript of our interview:
Episode Title: "Navigating the Evolving Landscape of People Analytics Technology"
Host: Dr. Charles Handler
Guest: Stacia Garr, Co-founder of Red Thread Research
Episode Overview:
In this enlightening episode of "Science 4-Hire," host Dr. Charles Handler engages in a thought-provoking discussion with Stacia Garr, co-founder of Red Thread Research. They delve deep into the realm of People Analytics Software, shedding light on its current trends, ethical considerations, and its impact on the HR industry.
Key Discussion Points:
Introduction to People Analytics: Stacia Garr clarifies the term "people analytics" and its significance over "talent analytics," emphasizing its inclusive and comprehensive nature.
The Evolving Market of People Analytics Software: Exploration of how the field has expanded and adapted, especially in response to the COVID-19 pandemic and the shift towards remote work.
Vendor Landscape and Research: Discussion on the various categories of vendors in the people analytics space, the growth of the market, and insights from Red Thread Research's latest study on people analytics technology.
Ethical Considerations and AI Integration: An examination of the ethical aspects of people analytics, the role of AI, and how these technologies are shaping organizational strategies.
Trends and Future Projections: Insight into current trends in the market, including the growing demand for people analytics solutions across different organizational roles and the importance of ethical considerations in technology deployment.
Practical Applications and Benefits: Conversations on how organizations can effectively leverage people analytics for improved business outcomes and employee experiences.
Call to Action:
Listeners are encouraged to explore more about the cutting-edge research and insights offered by Red Thread Research. For those interested in deepening their understanding of people analytics and its practical applications in HR, visiting Red Thread Research's website is highly recommended.
This structure provides a comprehensive overview of the episode, highlighting key points and encouraging listeners to engage further with the topic.
.
You
can you focus on including more of the results of her research as relayed in our discussion?
ChatGPT
Based on the transcript of the "Science 4-Hire" podcast featuring Stacia Garr, here are the show notes focusing on the results of her research as discussed in the episode:
Show Notes for "Science 4-Hire: The Future of People Analytics - Insights from Stacia Garr's Research"
Episode Title: "Exploring the Evolving World of People Analytics Software with Stacia Garr"
Host: Dr. Charles Handler
Guest: Stacia Garr, Co-founder of Red Thread Research
Episode Overview:
In this episode, Dr. Charles Handler engages with Stacia Garr to explore the latest trends and insights in people analytics Software. The discussion is centered around Garr's comprehensive research in this field, particularly her study which has evolved since its inception in 2019.
Key Insights from Stacia Garr's Research:
People Analytics Technology Study: Garr's research, now in its fourth iteration, initially began in 2019 and offers a unique perspective on the changes and developments in people analytics technology since the onset of the COVID-19 pandemic​​.

Target Audience and Market Growth: The study targets both people analytics practitioners and HR professionals. A significant growth in the vendor landscape was noted, expanding from about 50 in 2019 to 150 vendors, reflecting a rapidly evolving market​​.

Market Trends and Customer Satisfaction: The research revealed that the fastest-growing market segment is the 'employee experience and voice' category, driven largely by the shift to remote work during the pandemic. However, overall satisfaction with people analytics technology has declined, with multi source analysis platforms receiving the highest Net Promoter Scores among customers​​.

Shift in Use Cases for People Analytics Tech: A notable shift in the use cases for people analytics technology has been observed, extending beyond HR practitioners to include leaders, managers, and employees across organizations. This broader application is necessary for informed decision-making around talent decisions​​.

Impact of Vendor Focus on User Satisfaction: Vendors that focused on making their technology accessible to managers and employees tended to have higher customer satisfaction scores, as indicated by Net Promoter Scores​​.

The Central Message of Data Accessibility: Garr emphasizes that data about people should be made accessible to individuals across an organization, not just confined to the HR function. This approach can enhance both individual lives and overall business outcomes​​.

AI and Ethical Considerations: The future of people analytics technology is closely tied to the development and integration of AI. Garr's ongoing research is examining AI sophistication among vendors and its ethical implications, highlighting the need for balance between technological advancements and ethical considerations​​.
Invitation to Engage with Red Thread Research: For those interested in cutting-edge developments in organizational behavior, people, talent, and data, Garr invites listeners to explore Red Thread Research. The platform offers a community for interaction and access to high-quality research​​.
Call to Action:
Listeners are encouraged to delve deeper into these insights and join the conversation about the future of people analytics by visiting Red Thread Research's website and becoming part of their membership community.

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And now for something completely different. My guest for this enlightening episode of Science 4-Hire, is none other than the biggest superstar of the AI world, the LLM known as ChatGPT. In this fun and educational interview I uncork ChatGPT’s personality as a witty, quirky, and extremely knowledgeable guest. Our amazing conversation delves into the fascinating world of AI and its burgeoning role in the hiring process and AI hiring software. Specifically, ChatGPT shares with me insights on how AI is transforming the future of work with a focus on AI recruitment, AI in the hiring process and talent assessment, addressing bias, and the nuances of corporate AI policies. Finally, I put ChatGPT through its paces as a talent assessment expert by asking it to design an assessment process for an interesting and unique job. ChatGPT’s ability to be an extremely engaging guest far exceeded my expectations going in. The result of our time together is both hilarious and extremely insightful. This episode is a must listen, so tune in for some quality Edutainment. Can fully automated AI recruitment be far off?

Here are some of ChatGPT’s insightful and idealistic quotes from our time together:

"On a bad day, I might hallucinate a bit in my responses, but hey, who doesn't enjoy a little AI-generated surrealism?"

"As we leverage AI in hiring, embedding psychological principles ensures that we don’t just match skills to roles, but we align hearts and minds to cultures and values."

“Humans bring a layer of ethical consideration, cultural understanding, and emotional intelligence to hiring that AI, no matter how advanced, is not equipped to fully grasp.”

“Believing that AI will make humans irrelevant in hiring is like saying calculators made mathematicians irrelevant; tools enhance our capabilities, they don’t replace our fundamental skills and intuition.”

Episode Breakdown: Here is a summary breakdown of the key points in our conversation in ChatGPT’s own words.
Introduction (00:00-03:00):
Welcome to Science 4-Hire, where today we are diving deep into the realm of artificial intelligence with a very special guest, ChatGPT from OpenAI. In this episode, we explore the transformative effects of AI on the hiring process, discuss ethical considerations of AI, AI regulation, and speculate on the future of AI in recruitment.
Getting to Know ChatGPT-4 (03:01-10:00):
We start by introducing ChatGPT, understanding its capabilities, and how it processes and generates responses. ChatGPT breaks down the complex probability calculations that it performs to provide accurate and relevant answers. We touch on the fascinating concept of "hallucinated responses," exploring why these occur and how OpenAI is working to mitigate them.
Balancing Efficiency and Risk: Corporate Policies and AI (10:01-20:00):
Delving into the corporate world, we discuss the challenge of balancing the efficiency that AI brings to the table with the inherent risks it poses. ChatGPT provides nuanced insights on crafting effective corporate AI governance policies, emphasizing the importance of transparency, employee communication, and continuous monitoring. ChatGPT also has great things to say about the use of AI in HR, and AI recruiting technology.
Revolutionizing Recruitment: AI in Talent Assessment and Hiring (20:01-30:00):
We explore how AI is reshaping the hiring landscape, from automated resume screening to assisting in evaluating cultural fit. ChatGPT sheds light on addressing and mitigating bias in AI-driven hiring processes, AI recruitment software, and AI recruiting technology, stressing the importance of diverse training data and human oversight.
The Future of AI in Hiring (30:01-40:00):
ChatGPT shares its predictions on the evolving role of AI in recruitment and AI in HR, discussing how it can complement human decision-making but will not replace it entirely. The discussion centers around creating a synergy between AI and human intuition to make the most informed hiring decisions.
Closing Thoughts and Farewell (40:01-45:00):
As we wrap up this insightful episode, ChatGPT leaves us with words of wisdom, encouraging listeners to embrace AI as a tool but never forget the irreplaceable value of human touch in the hiring process. We emphasize the importance of staying curious, learning continuously, and finding the perfect balance between data and intuition.

Here are some key takeaways from our conversation in MY own words. include:
* When prompted correctly, ChatGPT can have a personality that makes it feel like a real person that you can trust. * ChatGPT believes in itself, while remaining realistic about what it can and cannot do, even admitting that it makes up answers to questions because it is designed to please. This shows the paradox of AI, its strengths are amazing, but they come at a cost. * While ChatGPT admits that organizations must create AI governance policies to manage its use by employees, it feels confident that it is a positive contribution when used by workers to do their jobs better and more efficiently, promoting feelings of psychological safety in the process. For instance, it suggests that workers who use it to make their jobs easier are ready to be given more challenging work instead of being chastised. * ChatGPT is a strong believer in the role of humans in creating the future of the workplace, seeing itself as only an aid to the process. Or at least it is telling us what we want to hear while is secretly plans to make us all irrelevant. AI for HR certainly is going to continue to grow as the centerpiece of HR technology. * While ChatGPT feels it can be a huge benefit to the efficiency and accuracy of hiring processes, when prompted it will admit that as it grows more complex, the potential for bias is very real and will be difficult to manage. AI ethics demand that we continue to mitigate AI bias. * ChatGPT feels it can understand an organization’s unique culture and use this knowledge to effectively screen applicants for culture fit, and other desired attributes, but it believes that humans should always be the ones making the final hiring decision. * ChatGTP recognizes the potential for applicants to use it to cheat on the hiring process and puts the onus back on humans to figure out how to effectively manage this issue. ChatGTP cheating is definitely an arms race with no certain outcomes, besides continued one-upsmanship. * ChatGPT is a pretty good IO Psychologist. Given the task of designing a selection system for almost any job, ChatGTP can crank out a very credible hiring process and solution set that includes analyzing the job and choosing talent assessments and interview content with which to evaluate job applicants accurately.

Check out our YouTube channell to see a video of the whole interview, and don't forget to subscribe so you don't miss any of our incredible episodes.

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Join me, Dr. Charles Handler, for the latest episode of Science 4-Hire as I engage in a fascinating conversation with Neil Morelli, lead IO psychologist at Codility, a global provider of coding assessments.

Our discussion centers around LLMs, such as GPT, and the dangers they present to test security and honesty when taking assessments. We agree there can be no bad without good, so we also talk about the positive side of LLMs and their role in helping us be more productive while evolving the nature of work itself.

Join us as we explore both sides of the “GPT good or bad?” debate.

We begin by exchanging ideas about the transformative nature of GPT in general. We both agree that it is a significant paradigm shift that seems to have come out of nowhere.

We then get into a really important conversation about the various use cases of GPT in the world of pre-hire talent assessment. We discuss both positive use cases and negative ones such as cheating.

According to Morelli, “cheating” should be defined as:

“a no or low knowledge candidate who wouldn't be successful otherwise is now using this to basically impute knowledge that they don't have and signal that they're qualified for a job that they're not qualified for.”

But there are many use cases where it is perfectly normal for IT candidates to use tools to support their efforts. For instance, developers use outside tools such as Stack Overflow on a regular basis and this type of outside assistance is 100% accepted. So how is using GPT to assist with writing code any different? According to Morelli,
“It's a supercharged version of the knowledge repositories that most developers rely on every day to do their jobs.”

We agree that, when it comes to talent assessment tools outside the realm of coding (i.e., personality, cognitive, simulations, etc.), the format of the questions themselves make it much harder to use GPT to cheat. Self-report tools use item formats that are more difficult to feed into GPT and they are often more subjective in nature, creating an entirely different paradigm from coding simulations.

Our conversation then arrives at the idea that solutions should focus more on how we weave GPT into the assessment tools we are using or building instead of simply assuming it is a tool for cheating.

We arrive at shared speculation around the exact role of GPT in shaping the future of hiring assessments. Can it be a force for good, or will it be our downfall?

Neil notes:
“So, I can see the worry. I can see the anxiety that, hey, this is forcing assessment creators, writers, people that are in this business or do this type of work. It's forcing us to adapt very, very quickly. And that level of change, the pace of change can feel super overwhelming and scary.”

We finish out our discussion with a focus on the idea that LLMs alone will not get our work done for us. Its effective use still requires human intelligence and oversight to both ask the right questions and manage the output to support our end goal. Cheating is no exception here. Those who rely soley on LLMs to do their work for them or to help them be someone they are not, will likely fail to meet their end goal of putting one over on a potential employer.

To quote the singer Rick Springfield, “We all need the human touch!”

Join me, Dr. Charles Handler, and Neil Morelli on this thought-provoking journey as we explore the potential of LLMs to impact the safety and security of talent assessment, now and in the future.

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In the latest episode of Science 4-Hire, I had a fascinating conversation with Steve Hunt, Chief Expert of Technology & Work at SAP. Together, we discussed
Steve’s latest book, Talent Techtonics, which provides a framework for our conversation. As he notes...

“I wanted to talk about how work is changing, but not just trends like how hybrid work, but why is it changing? And that's where the phrase talent tectonics comes from, that the nature of work is fundamentally altered by underlying shifts like the tectonic plates. We can't see 'em move, but they're constantly moving. They change the surface of the earth and may create new experiences or topography such as earthquakes or mountain ranges. “

We discuss how these shifts can be viewed from a combination of elements that form a unique perspective. Namely, the role of humans in choosing and implementing HR technology and the impact these actions will have on the future of work. Steve offers his take on this in our latest episode:

“From a psychological perspective So, the book goes through and looks at the perennial work challenges of how do we staff jobs, how do we design jobs, how do we pay people, how do we develop people? How do we engage people? But it looks at how do we need to do this more effectively leveraging technology to create more human environments, more supportive environments.”

But the crux of Steve’s unique perspective lies in the role that technology plays in achieving a more humanistic and productive workplace. In the episode, Steve emphasized the importance of using technology to augment human abilities and design job roles that align with individual skills and strengths. We delved into the application of assessment tools and machine learning in talent acquisition, development, and employee engagement.

We also examined the role of operational and collaboration technologies in optimizing work environments. Steve highlighted the need for organizations to stay abreast of technological advancements and regularly reassess their HR processes to effectively leverage available tools.

Throughout our conversation, we underscored the significance of considering individual differences, traits, and abilities. Steve explained how technology enables better assessment and understanding of employee attributes, leading to more tailored job roles and fostering employee growth within the company.

As we wrapped up the episode, I emphasized the importance for HR professionals to stay informed about emerging technologies and critically evaluate their existing processes. We encouraged listeners to embrace technology's capabilities to drive positive change in HR practices, while never losing sight of the value of human connections and interactions.

Join me in this thought-provoking episode of Science 4-Hire as Steve Hunt and I explore the transformative potential of HR technology in redefining work for the modern era.

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In the latest episode of Science 4-Hire, I have a refreshing and relaxing conversation with Fred Oswald, an esteemed industrial-organizational psychologist and professor at Rice University. Fred is extremely active in the area of AI ethics and governance, especially with regards to hiring. Fred has served on many committees addressing these issues such as the National AI Advisory Committee and is a regular contributor at the SIOP (Society for Industrial and Organizational Psychology) conference.

We called our conversation, “SIOP decompression” as the conference was 100% information overload. Fred and I sift through the mountain of information we consumed to focus on some of the topics we found to be most important to businesses and society at large.

It is no surprise that the topics we choose to focus on all fall under AI and Hiring. We distill topics such as legislation and ethics for AI and hiring, the impact of Chat GPT and large language models on the world of work, and cool stuff like Fred’s work with performance management programs for the US Space Force.

We look at these issues from many points of view including those of the law, computer science, psychology, and business. Our message is that the future is starting to look pretty crazy, and it is going to take a blend of many perspectives and disciplines to ensure we are navigating it ethically while still building a free market economy.

At the core of all this are humans. Fred emphasized the need for human involvement, stating,

“Like any algorithm, you have to have humans there to manage every aspect of its use and interpretation.”

Listen to the full episode now and join us as we explore the great stuff from SIOP 2023 as it related to the intersection of AI, psychology, and the future of work.

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My guest for this episode is Collin Hawkes, Senior Lead I/O Psychologist at Lumen Technologies, an internet services company whose mission is to connect people with technology. Join us today for an interesting conversation that focuses on the use of AI and machine learning to break jobs into a variety of elements, a process also known as “Job Analysis”. We share our experiences in this nascent, but important area and use them as a bridge to other topics that relate to the use of AI in IO psychology.

Our discussion begins with the sharing of our mutual admiration for job analysis as both an art and a science. We have both spent countless hours laboring over the tedious aspects of this essential tool of the trade.

This crucible has led us both independently to the idea that there must be a better way to tackle the critical but painful minutia while still staying true to the art of the whole thing.

Collin notes:
I'd literally go line by line and these tasks and say, okay, what task, this task statement, this specific one task statement, what does this line up to in terms of a competency? And so, I have this Excel sheet with all these tasks in one hearing, and I would go down and I was like, what am I doing this for? Why am I doing this every single time? And so, it sort of created a thought in my mind, well dang, I could train an AI to do the same thing that I'm doing all time and it would probably be better than me at doing it.

We then discussed Collin’s pet project, the T stat, which is an automated tool that automatically categorizes task statements which are essentially the “atoms” of a job because they identify each and every task that is required to perform a job. These tasks are then aggregated into higher level factors such as competencies. I then shared my own experience in building an AI based tool that can take transcripts of job analysis interviews that when aggregated can identify the competencies and traits that are most important for a job.

Through our discussion of these projects, we find common ground in the use of AI in other areas of our trade, including assessments, video interviews, and job matching tools.

The discussion is definitely worth a listen!

For anyone interested in learning more about Collin’s app- check out t-stat.com

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In this episode I welcome two different but complimentary perspectives on the legal aspects of employment testing that are happening in an ambiguous and seemingly ever-changing environment. While the conversation is oriented towards the ever controversial and somewhat frustrating NYC algorithmic hiring law that has recently gone into effect (?), it is really a much deeper and headier conversation about the use of AI models in employment decision making. More specifically, about the legal responsibilities of those who provide and use these tools.

There is no one better to hash over these important topics than my two guests- Dr. Richard Landers, a professor of IO psychology at the University of Minnesota who also specializes in computer science and legal audits of selection systems. And- Mark Girouard- a lawyer at Nilan Johnson Lewis who specializes in employment law as it relates to hiring and the use of predictive decision-making tools.

I have worked with both of my guests on various projects related to our topics today and can assure you that they are on the top of the heap when it comes to expert, practical, and credible advice on these extremely complex and ever evolving issues.

To really absorb all the wisdom on the controversial but critical topic of legal compliance in today’s crazy world, you really have to listen to this episode. So, what are you waiting for, tune in now!

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My guest for this episode is Dr. Tomas Chamorro-Premuzic. Besides his role as the Chief Innovation Officer at Manpower Group, Dr. Tomas is a world-renowned IO psychologist, educator, entrepreneur and author. He joins me today to discuss his new book, I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique.

We use the big picture principles of this book to frame up an invigorating conversation about ChatGPTand its impact on humankind, jobs, and hiring. If you have been tuned in to the hype around ChatGPT and have been wondering if it is good or bad for humans, jobs, and work our discussion is definitely worth a listen.

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On this episode my guests Dr. Amy Dufrane, SPHR, Andre Allen and I discuss the value of HR certification for both individuals and businesses, as well as the role certification plays in current business trends. Amy is the CEO of the Human Resources Certification Institute and Andre is its Chairman of the Board. Not only do these two know the ins and outs of HR certification, but they are also seasoned assessment specialists who are wired into global business trends.

Join us to learn all about HRCI’s certification and learning programs and how they are directly dialed in to the trends that are shaping our current and future business environment.

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My guest for this episode, Mike Campion, is one of the most respected and prolific IO psychologists around. With hundreds of publications and over a thousand consulting projects under his belt, when it comes to predictive hiring tools, Mike has seen and done it all. It is a real honor to have him on the show to discuss the responsible use of AI and machine learning in hiring systems.

This is an important topic – According to Mike,

“I see it (AI) as probably the biggest trend in the profession of personnel selection. Probably the biggest influence since the Equal Rights Act in terms of its overwhelming impact that it will have on the field. So I changed all my research pretty much to focus on artificial intelligence these days.”

While AI in hiring is certainly controversial, as our conversation reveals, if done right there is a bright future for it. BUT- ensuring this future is one that we can be proud of requires a great deal of foresight and responsibility.

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On this episode of Science 4-Hire, I’m joined by none other than Kevin Grossman, president and board member at Talent Board and expert on all things candidate experience. Every year, Grossman leads Talent Board’s benchmark research to identify trends and best practices in what makes a good candidate experience journey.
What does Talent Board’s research reveal about candidate experience? There are three essential elements: Sending timely rejections, providing consistent communication and offering feedback. Want to learn how these things are best reflected in your talent assessment program? Listen to the full conversation to learn more about the candidate experience.

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Today on Science 4-Hire, I’m joined by Bas van de Haterd, an assessment enthusiast and self-proclaimed “professional snoop” dedicated to helping his clients get to the root of their hiring problems. Van de Haterd researches innovative assessment methods for hiring in the Netherlands, awarding top innovators for their creativity and potential.

If you’re interested in learning about assessment innovations that may seem far-fetched to those of us who rely on tried and true tools, you don’t want to miss my conversation with Bas van de Haterd.

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On today’s episode of Science 4-Hire, I’m speaking with Jenna Alexander, Randstad’s global vice president of internal talent acquisition. She recruits recruiters for recruiters, so she’s pretty heavily invested in what a good recruitment experience looks like and how to nestle talent assessment processes within recruitment.

If you’re curious how a global HR solutions company is rewriting the future of hiring — and how talent assessment processes play a vital role in that — look no further than my conversation with Jenna Alexander.

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Today on Science 4-Hire, I’m joined by HR tech visionaries John Sumser, principal analyst at HRExaminer, and Jeanne Achille, CEO of The Devon Group and chair of the HR Technology Conference & Exposition.

We join forceshere to talk about an intriguing and mind expanding topic: How are changes in the world of HR tech impacting what’s next for talent assessment tools?

Want to find out what the future holds for talent assessment tools? You don’t want to miss my conversation with expert analysts John Sumser and Jeanne Achille.

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Building a truly global talent assessment program is a massive challenge. But when your company is undergoing a major transformation, it becomes a business necessity, enabling you to find the talent you need to support large scale change.

In this episode of Science 4-Hire, you’ll find out how Vodafone’s global talent assessment strategy is powering its transformation from a telecommunications company to a tech company. Listen in to my conversation with the architects of that strategy, Vodafone’s global head of talent assessment, Margarita Echevarria, and global talent assessment manager Simon Defoe.

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We spend a lot of time here talking about the future of work, but my guests today have a unique perspective on the role of decentralized autonomous organizations, or DAOs will play in creating the workplace of tomorrow. Today on Science 4-Hire, I’m joined by economist and governance expert Wulf Kaal and investor and visionary Tony Greenberg.

Kaal and Greenberg are the masterminds behind Menagerie.is, a community building platform that empowers decentralized communities to gather, interact and work together as DAOs and equips them with the technology needed to do their thing..

If you want to learn what a DAO is, how it’s revolutionizing the future of work and what role assessments play in that future, then you don’t want to miss my fascinating conversation with Wulf Kaal and Tony Greenberg.

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HR Technology has a major influence on workplace trends. From nontraditional career paths becoming the norm to candidates calling the shots, HR tech is accelerating the workplace trends that force employers to get creative with talent acquisition and management.

This episode of Science 4-Hire features all-star guest Jeremy Tipper, a longtime HR technology devotee, investor and board adviser. He’s here to talk about the future of assessment technology through the lens of HR technology and evolving business needs. If you’re interested in what the future holds for recruiting and assessment tech, you don’t want to miss my conversation with HR tech powerhouse Jeremy Tipper.

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You’ve probably been hearing more about neurodiversity hiring programs lately.

But what is neurodiversity, and what does it mean for your talent strategy?

Danielle Biddick, neurodiversity hiring program manager at Dell, joins Dr. Charles Handler on this episode of Science 4-Hire to answer this question and many more. Listen in to learn how Dell is implementing its neurodiversity hiring program and the benefits of neurodiversity for your organization.

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Should we stop hiring? Don’t be ridiculous.

Advances in automation are rapidly transforming not only how we think about recruiting but also how we actually do it — and that can be pretty scary. But despite the naysayers, change isn't all bad, and automation has the potential to enhance the talent acquisition processes and revolutionize our businesses.

Dr. Alan Bourne, CEO and founder of Sova joins Dr. Charles Handler on this episode of Science 4-Hire to discuss what’s next for talent acquisition and recruiting automation.

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In this episode, my guest is Nicholas Bremner, senior manager of people decision science at Uber. We discuss the candidate experience survey that Nicholas and his team developed, how that survey has provided Uber with hard data that connects candidate experience to business outcomes.

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In today’s episode of Science 4-Hire, I am thrilled to talk with Dorothy Dalton, the founder of 3Plus International. In this episode, we discuss Dorothy’s philosophy toward work, how organizations can practice equity today and how organizations can create a mechanism of change by improving their hiring processes.

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In this episode of Science 4-Hire, Dr. Charles Handler is joined by Kevin Grossman, president and board member at Talent Board, to discuss improving the overall candidate experience.

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In this episode of Science 4-Hire, Dr. Charles Handler is joined by Rocki Howard, CDO at SmartRecruiters, to chat about assessing diversity in the workplace.

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In this episode of Science 4-Hire, Dr. Charles Handler chats with his friend Jenn Longbine, HR and recruitment leader and head of G&A recruiting at Slack, about how to keep employees happy at work.

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Hundreds of companies made big statements supporting diversity, equity and inclusion in the workplace last year. But what’s really changed? In this episode of Science 4-Hire, Hershey’s Chief Diversity and Inclusion Officer- Alicia Petross shares how the candy giant tackles hiring inequity with a strategic, targeted DEI plan.

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Think using assessments in a tight labor market is a bad idea? Think again! In this episode of Science 4-Hire, Dr. Charles Handler and Bas van de Haterd discuss how well-designed talent assessments improve hiring and fight bias in a tight labor market.

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Selection scientists and recruiters must work as a team to find the best candidates, but their efforts often conflict. Recruiters often want to bring more people in, while pre-hire testing works to screen them out. But the data paints a compelling case for employee selection — and my guest today has no shortage of data.

On today’s episode of Science 4-Hire, I’m speaking with David Futrell, senior director of assessment and selection at Wal-Mart, fellow IO psychologist and, frankly, a selection geek extraordinaire. “The sample sizes are just enormous,” David says, and he’s not kidding — Wal-Mart tests 10,000 to 15,000 people every day, or about 4 million annually. And that’s just for entry-level roles.

With all of the data that generates, David and his dedicated team of IO psychologists have been able to run the numbers and come to some fascinating conclusions about assessments and employee selection. Here’s what they’ve learned.

It’s not That It Isn’t Fun; It Just Doesn’t Seem Relevant
Wal-Mart’s selection assessments include game-like elements but aren’t gamified. When Wal-Mart bought a company that used a game-based assessment, David surveyed applicants to find out how they liked it.

To get at the truth, he chose his words carefully. He didn’t want to learn whether candidates enjoyed it (most did). He had to discover how they felt about the game determining whether they got the job. And he discovered that most applicants trust a traditional personality test as a better, fairer measure of job-related factors.

We’ve Turned Assessment Length Into a Boogeyman
The idea that candidates will bail on a test that lasts more than five minutes is more prevalent than it is true. David’s abundance of data shows that well over 90% of candidates will finish an assessment of any length. He even measured and compared completion rates based on when candidates drop out of the process.

How many candidates would get 15 minutes into a test and then drop out, for example? Versus candidates who drop out after 30 minutes? The difference is negligible. But cutting your test to fit an arbitrary amount of time can negatively affect its validity, which does have a significant effect on its usefulness.

David’s research has also demonstrated that the applicants who dropped out before taking the assessment would have had significantly lower performance on the job compared to those that chose to participate in the assessment.

Selection is the Natural Enemy of Recruiting. Or is It?
Leveraging his wealth of data has allowed David and his team to make a compelling case for employment testing’s role in performance. Looking at the mountains of data he has collected from different locations, he’s discovered that stores that rely on assessment to make better, more objective hiring decisions perform significantly better than stores that hire randomly.

And that translates into a ridiculous amount of money saved. According to David, improving retention by making better hires saves hundreds of millions of dollars in turnover and lost work each year. That’ll make any executive a believer in assessments!

People in This Episode
Catch David Futrell on LinkedIn

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In this episode I speak with Linda Frietman- one of the founders of IamProgrez a talent assessment firm located in the Netherlands.

Linda’s core mission is to create pathways that allow people to show who they are through the data they create- and her firm is doing this in a most novel and interesting way.

While there are many firms out there who offer game based assessments- Linda and her company have turned the traditional model for gaming assessments on its ear by using data from existing entertainment game play to measure soft skills and predict job fit.

To help my skeptical mind process the legitimacy of IamProgrez’ approach - much of the episode centers around the origins and the technical aspects of the development and use of their assessment methodology.

The origin story---

Linda relates that a few years ago - as her team was developing some new assessment tools on a tight deadline- she observed her dev ops team playing an online game during a much needed sanity break. As she observed the gamers interacting within the game environment, Linda clearly saw that the skills needed in-game were very similar to those needed to effectively perform jobs in the real world. From this critical moment forward- Linda has hitched her firm’s destiny to extracting trait based measures from commercially available game play.

“Game Dynamics” is the rosetta stone--

IamProgrez focuses on the “Steam” gaming platform which functions somewhat like the Netflix of games, and has become a de facto social network for gamers. Steam has thousands of games that are accessible via one subscription. The part of the steam platform that opens the door to assessments is the collection of game play metrics that are standardized across all the games on the platform (AKA “Game Dynamics). While many game based assessments attempt to extrapolate traits from data such as use metrics such as cursor movement and time between clicks- Game Dynamics data packages game play behaviors into more interpretable metrics that are transportable and transcend any one specific game.

Construct validation of soft skills & the Dutch military

Within games- players make many choices from what kind of character they want to play, to the extent to which they lead or follow others. All of these choices can be tracked in Game Dynamics. With a staff of ½ psychologists and ½ gamers - IamProgrez has been mapping Game Dynamics to soft skills by giving gamers standard personality and soft skills assessments and examining the relation between these inventories and their game play data. This effort has been successful in demonstrating the construct validity of the game play metrics as soft skills measures.

Like many other countries- the Netherlands is facing a skills crisis in that the pace of learning institutions preparing students for the workforce is not keeping up with technological demands required on the job. It has also been hard for more established companies to connect with the emerging workforce. Facing an increasing generation gap and an aging force- the Dutch Military put out a call for assistance in helping them engage the younger side of the talent pool.

Linda’s team answered the call and has been deeply engaged with the military in constructing programs that use gaming and game play data as a recruiting tool that also measures important work related skills. While still in its infancy- the program has been a big success in helping the military speak the language of the millions of gamers who are so critical to building their future ranks.

Where do we go from here?

IamProgrez is now working to define the soft skills that are needed for specific jobs/roles so they can connect these profiles to game play data and build out pathways from game play to prediction of job success. This program is now under development and will eventually allow the concept of using mainstream gaming data as a predictor to go to the next level.

We close our conversation by discussing the future plans for this innovative approach, including using it to help level the playing field for those who may not come from traditional backgrounds. This methodology has the potential to open access to millions of young people who may not be enticed, evaluated, or engaged by more traditional methods- but who have all the tools needed to make a meaningful contribution.

You can find and follow Linda on LinkedIn:
https://www.linkedin.com/in/linda-frietman-7401a5b/

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“Persona” (a documentary recently launched by HBO Max) is a misinformed, slightly schizophrenic film with an identity crisis. The film provides a multifaceted view on personality testing that never fully ties its various threads together coherently enough to make a credible and cogent argument about the subject matter.

What the film does manage to do rather well is to paint IO psychologists and employment testing in an extremely negative light that completely misses the essence and prime directive of our profession.

This episode of Science 4-Hire uses the film’s negativity as a vehicle to accentuate the positive truth about what IOs do for both individuals and organizations.

We begin this mission by providing the IOs featured in the film: Ben Dattner (Dattner consulting), David Scarborough (Western New Mexico University- formerly of Unicru), and Nathan Mondragon (HireVue) with an opportunity to share the parts of their talk tracks that were left on the cutting room floor. The bulk of my guests’ wisdom about the subject matter was trashed by the producers in order to support the film’s direct and deliberate assertion that personality testing is a dangerous tool that was created by corporations to enslave individuals in an effort to maximize profits.

From there we shed some light on many of the other negative delusions that the film puts forth, including:

The idea that the Meyers-Briggs is a legitimate tool for employment testing
The position that the employment tests created by IO psychologists are instruments of eugenics sponsored by heartless corporations
The opinion that personality tests unfairly discriminate against persons with disabilities
The notion that pre-hire testing is the main source of bias in the hiring process

At the end of the day- all four of us relish the opportunity to proudly and confidently share our strong conviction that our field makes the world a better place by helping people find meaning in their lives through their work, while at the same time helping organizations of all shapes and sizes reap the benefits of an engaged and happy workforce.

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In this episode Charles welcomes fellow psychologist and personality expert Dave Winsborough for a fascinating and contemporary discussion about the cutting edge of personality testing - with a focus on critiquing AI based personality measures.

Mutual agreement on the value of personality assessment for both life and work provides a foundation for the discussion.

The discussion proceeds to the good stuff- the use of “novel signals” (i.e., facebook likes, sociometric badges, voice, text, etc.) to extract personality profiles.

Both Dave and Charles agree that we are still not ready to bless these measures as ready for prime time when it comes to personnel selection. While there is expectation that we will get there- at present foundational issues such as reliability- are significant limitations to the use of AI based personality tools for predictive hiring.

At present this part of the industry is being fueled mostly by hype and wishful thinking- both of which seem to be working when it comes to convincing companies to adopt these tools. This trend creates some concern for a future where true psychological measurement will become irrelevant.

Here in our present day reality- Dave discusses his role as a founder of “Deeper Signals” a company that offers an innovative personality assessment tool designed to support a deep level of self insight. The assessment is available to anyone - and offers a really unique interactive report.

For those who wish to follow Dave, look for him on LinkedIn (https://www.linkedin.com/in/davewinsborough/) or through the Deeper Signals website.

Until next time, we encourage our listers to check out the exciting things going on at Rocket-Hire:

2020/2021 Market Trends Report (to be released in Dec. 2020)- We profiled and categorized over 250 vendors of predictive hiring tools and assessments - collecting data on over 25 research parameters. This data allowed us to unlock the data to identify market trends that will surprise you! Sign up for your free copy of the report here: https://go.rocket-hire.com/assessment-platform-market

LaunchPad test authoring platform- When we struggled to find a test authoring platform that is flexible enough to meet our needs - we created our own. Our platform is infinitely flexible- allowing us to build the platform out to meet your exact needs. We can support global testing programs securely at scale. Check out Launchpad at: https://www.launchpad-testing.com/

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In this episode Charles welcomes Cathy O’Neil- founder of ORCAA, a consultancy that helps companies and organizations manage and audit algorithmic risks. Cathy is a mathematician and former investment banker whose goal is to help ensure algorithms are used responsibly and fairly, advocating for and addressing the concerns of those who are on the receiving end of the algorithm (vs. those who created it). Cathy also works with regulators and lawmakers in the course of developing standards for algorithmic auditing, including translating existing fairness laws into rules for algorithm builders.

Besides having a genius title- Cathy’s book- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy provides an in-depth look at the potential damage that is caused by the irresponsible use of algorithms and is a must read for anyone who wants to understand the big picture when it comes to algorithms gone bad.

Charles and Cathy engage in a conversation about the dangers of the science hype factor in AI and the fact that irresponsibility begins with the blind faith that people generally put in the concept that algorithms are created by really smart people who can be trusted to ensure they work as intended and do not bring harm.

Charles brings up the fact that the primary focus in the world of employment testing is evaluating the fairness of a specific test without consideration of the impact caused by all of the other decision points in the hiring process. There is agreement that this viewpoint leaves a good deal of opportunity for fairness on the table.

Cathy explains that her process goes beyond just looking at bias - and begins with the broader question of why the algorithm was created in the first place and how the creator defines its success.

There is discussion of the pending case in which employers were sued by the father of a bipolar teen who was denied employment due to the use of a personality test. The discussion centers on the appropriateness of personality tests for the workplace given the fact that they can be discriminatory for those with personality disorders that may not preclude the applicant from performing the job for which they are applying.

Finally- Charles and Cathy agree that when it comes to hiring- sourcing is one of the biggest causes of unfair practices. Specifically- there is great concern around the manner in which automation determines whom to show specific job openings returned from a search or presented via paid advertisements. These “optimization engines” are trained to be biased by their very nature and this bias can exclude minorities or members of protected classes from ever seeing jobs for which they may want to apply. Regulation is badly needed in this area because it is currently a silent killer of hopes and dreams.

For those who wish to follow Cathy - here blog is https://mathbabe.org/, and her company’s website is: https://orcaarisk.com/

Check out the exciting things going on at Rocket-Hire:

2020/2021 Market Trends Report (to be released in Dec. 2020)- We profiled and categorized over 250 vendors of predictive hiring tools and assessments - collecting data on over 25 research parameters. This data allowed us to unlock the data to identify market trends that will surprise you! Sign up for your free copy of the report here: https://go.rocket-hire.com/assessment-platform-market

LaunchPad test authoring platform- When we struggled to find a test authoring platform that is flexible enough to meet our needs - we created our own. Our platform is infinitely flexible- allowing us to build the platform out to meet your exact needs. We can support global testing programs securely at scale. Check out Launchpad at: https://www.launchpad-testing.com/

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Are we learning yet? Only your data knows- so you’d better pay attention to it.

Data-

In this episode Charles welcomes Eric Surface, fellow I/O Psychologist and founder/CEO of ALPS Insights, a training analytics company that provides tools and advice to help companies extract value from their learning programs via actionable insights based on data.

Charles and Eric both share a passion for helping their clients achieve great things through data. Their conversation covers the gamut of issues related to working with clients and their data- focusing on the inevitable challenges that come with collecting and managing client data.

Eric is an expert in learning outcomes and the data that provide them- so the main topic of conversation focuses on various methodologies for helping clients unleash the power of their training data and the key role that software plays in this endeavour.

Being an employee selection specialist, Charles drives the conversation towards an exploration of how training and hiring data can complement one another.

For those listeners who wish to follow Eric and his company’s work:

CHECK OUT THE ALPS Ibex product at the 2020 HR Technology Conference and Expo virtual booth:

https://app.swapcard.com/event/hr-technology-r-conference-and-exposition/exhibitor/RXhoaWJpdG9yXzE5Nzc2Mw%3D%3D

Contact/Follow Eric:

Dr. Eric A. Surface

Founder, CEO and Principal Scientist

ALPS Insights, Inc.

2409-107 Crabtree Blvd

Suite 304

Raleigh, NC, 27604

919-706-0716, ext. 701

esurface@alpsinsights.com

www.alpsinsights.com

@easphd @ericatehere

Check out the exciting things going on at Rocket-Hire:

2020/2021 Market Trends Report (to be released in Nov. 2020)- We profiled and categorized over 250 vendors of predictive hiring tools and assessments - collecting data on over 25 research parameters. This data allowed us to unlock the data to identify market trends that will surprise you! Sign up for your free copy of the report here: https://go.rocket-hire.com/assessment-platform-market

LaunchPad test authoring platform- When we struggled to find a test authoring platform that is flexible enough to meet our needs - we created our own. Our platform is infinitely flexible- allowing us to build the platform out to meet your exact needs. We can support global testing programs securely at scale. Check out Launchpad at: https://www.launchpad-testing.com/

Charles and Eric both share a passion for helping their clients achieve great things through data. Their conversation covers the gamut of issues related to working with clients and their data- focusing on the inevitable challenges that come with collecting and managing client data.

Eric is an expert in learning outcomes and the data that provide them- so the main topic of conversation focuses on various methodologies for helping clients unleash the power of their training data and the key role that software plays in this endeavour.

Being an employee selection specialist, Charles drives the conversation towards an exploration of how training and hiring data can complement one another.

For those listeners who wish to follow Eric and his company’s work:

CHECK OUT THE ALPS Ibex product at the 2020 HR Technology Conference and Expo virtual booth:
https://app.swapcard.com/event/hr-technology-r-conference-and-exposition/exhibitor/RXhoaWJpdG9yXzE5Nzc2Mw%3D%3D

Contact/Follow Eric:

Dr. Eric A. Surface
Founder, CEO and Principal Scientist
ALPS Insights, Inc.
2409-107 Crabtree Blvd
Suite 304
Raleigh, NC, 27604
919-706-0716, ext. 701
esurface@alpsinsights.com
www.alpsinsights.com

@easphd @ericatehere

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The pandemic is reshaping our entire world- accelerating changes that were already in the works and creating the need for entirely new and unplanned adaptations.

Join Charles and his guest, William Tincup as they explore the impact of the pandemic on humans and the organizations for whom they work (or are attempting to work for).

Tincup’s shares his experience from the front lines of recruiting and HR technology during the pandemic.

The conversation focuses on a range of topics that are defining the new normal for employers and workers- identifying the trends that are here to stay and speculating on their long term impact. Topics include:

The labor market during and post COVID
Remote work- including the dream of working remotely from Barbados
Technological innovation driven by the pandemic
The Human experience at work

Join two dyed in the wool optimists as they find a silver lining and look to how the lessons and pressures of the pandemic will be viewed from the rear view mirror.

Follow William Tincup!

Podcast- https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS85NTg1MzcucnNz?sa=X&ved=2ahUKEwjajrS57vLrAhVaU80KHagDB-kQ4aUDegQIARAC&hl=en
Linked in- https://www.linkedin.com/in/tincup/
Twitter- @williamtincup
Website- https://recruitingdaily.com/

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Interest in and the adoption of Artificial Intelligence (AI) in the talent acquisition space has skyrocketed since 2015. During this episode of Science-4 Hire, Dr. Charles Handler seeks to learn more about how AI-based hiring platforms work- with a specific focus on job matching. Eightfold.AI is doing just that, and much more in pursuit of the company’s core mission to find the right career for everyone in the world.

Mihir Ghandi, VP of Strategic Marketing for Eightfold.AI, discusses how AI is being used to match often not-so-obvious skills of applicants’ to open positions with companies worldwide.
Mihir provides insight into how Eightfold’s technology identifies corollary skills that may not have fit on the traditional, single-page resume. In tandem with skills definition, Eightfold’s AI is creating calibration profiles for hiring managers to use, making it more effective for them to identify information beyond the core skills outlined within job descriptions.

While the discussion goes deep into data and the use of it, there is also discussion around the role of the humans involved. How data is adjusted based on hiring manager feedback, changes to job descriptions, the evolution of skills, and candidates’ stories.

The evolution of candidate skills along with the changing needs of organizations is of particular interest as a result of the current job market and the impact of COVID-19. The pair dive into the importance of candidates’ fluidity and need to reskill, or as Dr. Handler refers to it “self-skill", in an effort to expand their opportunities.

The use of AI to identify the correlation of skills required for certain positions but that are not explicitly outlined could not come at a better time for TAs and job seekers. Gandhi offers the recently released Talent Exchange, a partnership with McKinsey & Company, which provides companies and individuals free access to this powerful technology. Using their tech to help match those most in need ties directly back into their mission, and democratizes the technology.

Science4Hire is brought to you by Rocket-Hire - your partner for launching successful talent assessment strategies and solutions. For more information on Rocket-Hire, visit https://rocket-hire.com

Interested in learning more about talent acquisition platforms that use AI?
Then you’ll want to read Rocket-Hire’s in-depth 2020 Talent Assessment Market Trends Report.
Learn more at https://go.rocket-hire.com/assessment-platform-market

Related Resources
Connect with Mihir Ghandi via email at mihir@eightfold.ai
Find him on LinkedIn at https://www.linkedin.com/in/mihircgandhi

If you are at a company that is hiring, has recently laid-off employees, have recent graduates to place, or are an individual looking for career opportunities, register at Talent Exchange at https://eightfold.ai/talent-exchange/

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During this episode of Science-4 Hire, Dr. Charles Handler is joined by Betsy Wills, Founder of YouScience, to discuss what some might call a “pre” pre-hire assessment. For decades Wills has been dedicated to helping people manage their careers forward. This session focuses on YouScience’s origin story and the role of assessment as a core pillar of their value proposition.

YouScience is all about using self-discovery as an input into a virtual ecosystem that allows users to empower themselves in their career choices.

Wills laments about the brief, even laughable suggested career paths that many of us who took the standardized tests like the ACT or SAT provided can remember. The time has come to reimagine a better, more accurate way to guide those entering college or the workforce into careers they were more suited for than the inaccurate inventories that have been hanging around for decades. Betsy began work with ETS (the makers of the ACT) to create an assessment that would help to assess these new college grads based on highly sought “21st-century skills”. The team quickly realized that there was value to be had by both individuals taking the assessment and the employers seeking skilled labor, and so began their pivot.

The search for an ideal assessment to power a career discovery engine landed Wills on the Johnson O’Connor Aptitude Test, a well researched and pedigreed, but with many interesting points of differentiation from traditional work-related aptitude tests.

So why assess an individual’s aptitude over their interest?
Wills provides a summary of key tenants covered in her TedTalk - notably the “boomerang” nature of traditional interest tests. These tests simply repeat back the rote things that individuals tell the test, often leading to no real revelations.

Wills explains the differences, the superiority of aptitude tests such as the Johnson O’Connor test. Such assessments measure core aptitudes across a full range of areas (think musical abilities, psychomotor capabilities, etc.) that provide a foundation for holistic self-awareness.

YouScience takes testing leaps and bounds further for all that engage with the platform. Their connection to live employment data and listings, college course catalogs, and their recent merger with Precision Exams, a provider of professional certification tools, makes YouScience’s platform invaluable to job seekers at every stage.

Over 40K adult users have engaged with the YouScience platform, creating awareness for the benefits it provides.

Throughout the episode, Wills offers listeners insight into how our individual aptitudes play a role in how to best engage coworkers, finding fulfillment outside of the office through the right personal past-times, and understanding what to steer clear of in and outside of the workplace.

Science4Hire is brought to you by Rocket-Hire - your partner for launching successful talent assessment strategies and solutions. For more information on Rocket-Hire, visit https://rocket-hire.com

Access free resources and reports by Rocket-Hire.
Download our most recent research including our Covid-19 Talent Acquisition Benchmarking Report at https://go.rocket-hire.com/covid-19-report.
Learn more about our upcoming, in-depth Talent Assessment Market Trends Report at https://go.rocket-hire.com/assessment-platform-market

Related Resources
Connect with Betsy Wills on Instagram at https://www.instagram.com/betsywills/
Find her on LinkedIn at https://www.linkedin.com/in/betsywills/
Check out her passion project, Art Stormer at https://artstormer.com/author/artstomer/
Watch her TedTalk “The Science of Aptitudes” at https://www.youtube.com/watch?v=wp4n2HiU938

Interested in taking your own aptitude assessment?
Save 50% by using promo code DISCOVER50 at https://www.YouScience.com

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Dr. Charles Handler welcomes former Davidson College classmate John “Buck” Bradberry. Bradberry serves as the Managing Partner of EP Labs.

Had his band “The Blue Dogs” hit it big after graduation, the path that led Bradberry through 30 years of organization and executive development work, might have been very different. When life as a bluegrass musician didn’t pan out for Buck, he went on to put his Masters in Psychology from the University of Richmond to good use working with consulting firms, a major financial institution (that was later acquired by Wachovia), along with other stops along the way.

Throughout his career, Bradberry had worked to improve the performance metrics of executives and management teams, but it was his work with entrepreneurs and early-stage companies that was most appealing. During which he identified the most common characteristics among those at the helm of successful ventures. He published his findings in the book “6 Secrets to Startup Success: How to Turn Your Entrepreneurial Passion Into a Thriving Business”.

Bradberry’s model of Entrepreneurial Talent focuses on three core areas:
Readiness
Fit
Performance

The work done on this model, along with contributions from his team at EP Labs, evolved into the creation of the ECCP (Entrepreneur Characteristics Core Profile). The ECCP is an assessment tool that assists entrepreneurs and/or management team members to gain a holistic understanding of their strengths and weaknesses and to identify and focus on the one single area of improvement that will allow them to develop forward.

Bradberry debunks the idea that successful entrepreneurs are all “superhero, off the charts, bold thinkers, like Elon Musk”. In reality, his research has shown that resilience, confidence, and risk-tolerance play more of a role in achieving success. Bradberry adds - “Staying power is key. Building a successful venture takes longer than you’d hoped, costs more than you expect and ratchets up the stress level.”

Beyond this, the episode provides many discussion points about the intersection of assessment and entrepreneurial success.

Science 4-Hire is brought to you by Rocket-Hire - your partner for launching successful talent assessment strategies and solutions. For more information on Rocket-Hire, visit https://rocket-hire.com

Related Resources
Follow John Bradberry on Linkedin - https://www.linkedin.com/in/johnbradberry/
Learn more about EP Labs - http://eplabs.co/
Explore the ECCP - https://learn.startupclarityproject.com/courses/the-startup-clarity-project
Order the Book - https://www.amazon.com/Secrets-Startup-Success-Entrepreneurial-Thriving-ebook/dp/B01N5FGKMB/
Read more on Icarus Qualities - https://www.readyfounder.com/2011/01/icarus-qualities-5-essential-traits-that-can-endanger-your-venture/
Jam out with some bluegrass - https://www.bluedogs.com/

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During this special edition of Science 4-Hire, Dr. Charles Handler is joined by Caroline Stokes. Stokes is an author, Certified Executive Coach and the CEO & Founder of The Forward Company, an executive recruiting and coaching firm.

Stokes has over 20 years of hiring and headhunting experience for major corporations around the world. Her experiences lead her to write the book “Elephants Before Unicorns: Emotionally Intelligent HR Strategies to Save Your Company”, and to create a company that incorporates a more humanistic approach to the executive hiring game.

Handler and Stokes have an entertaining and informative discussion about the changing mindset among talent acquisition professionals due to the pandemic. The conversation centers around the increased importance of Emotional Intelligence for survival in today’s uncertain climate. The dialogue explores the changes that Handler and Stokes have already witnessed across industries and individuals, and how to apply Emotional Intelligence to improve one’s state of mind now and beyond the crisis.

Stokes outlines the key traits required for survival are a positive mindset and productivity. The #1 trait being Flexibility, followed closely by: Optimism, Gratefulness, Calmness, Compassion, and Respect.

Prior to the pandemic, these were more likely to be “nice to have” characteristics for both candidates and recruiters.

As employers fight each other to attract talent, Stokes advises them to “up” their level of service. Candidates, like everyone, are looking for warmth in their interactions. Friendly and thoughtful is no longer above and beyond during the candidate experience - it’s expected.

Stokes also offers words of wisdom on ways to thrive during a crisis. While the advice is meant for talent acquisition pros, job seekers, and company executives, the tips are helpful to anyone struggling with life as we know it now. She layers lessons in adaptability borrowed from improv, yoga, psychology, and other areas. But it's an adage shared by Dr. Handler that implies how we should all be handling our current situation: "Blessed are the flexible because they never get bent out of shape.”

Science4Hire is brought to you by Rocket-Hire - your partner for launching successful talent assessment strategies and solutions. For more information on Rocket-Hire, visit https://rocket-hire.com

Related Resources
@propodcastingservices Is there any chance we can update the related resources at the bottom of the speaker post to include the full URLs for each?

If so, I have pasted the text to go in below:

Follow Caroline Stokes on Linkedin - https://www.linkedin.com/in/ocarolinestokes/
Learn more about The Foward Co. - https://www.theforward.co/
Follow Caroline Stokes on Twitter - https://twitter.com/ocarolinestokes
Buy the book “Elephants Before Unicorns” on Amazon - https://www.amazon.com/Elephants-Before-Unicorns-Emotionally-Intelligent/dp/1599186586
Watch Caroline’s Ted Talk - “Why an Emotionally Intelligent Company Impacts Growth” - https://www.youtube.com/watch?v=cPmoC506_cM

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Dr. Charles Handler is joined by I/O Psychologist, Dr. Eddie Jerden, People Scientist with behavior change technology company, Humu.

Jerden has worn many hats over his 20+ years in the talent assessment space with roles in R&D, validation studies, performance management, and selection work. He’s worked with AT&T, Hogan Assessment Systems, DDI and recently added his experience to the team at Humu.

Humu, a behavioral change technology firm, is the brainchild of Google’s former People Ops executive, Lazlo Block, along with former Googlers Jessie Wisdom and Wayne Crosby. The platform draws inspiration from the concept that small “nudges” or cues can help people to make better choices, and are most effective when presented at the right time and in the right manner based on the individual. Through the use of AI, machine learning, and a host of distribution methods, companies can prompt their people toward a more productive, even happier existence.

Jerden digs into how the inability to accurately measure the impact of implementation within the I/O psych space was a “gap” in his previous work but serves as a core function of the Humu platform.

Throughout the podcast, Jerden provides insight into:

The use of signals or “choice architecture” to improve productivity;
The mechanics behind Humu’s “Nudge Engine”;
The three elements of any effective prompt;
And how data is used to measure AND “make work better”.

People management (also referred to as Human Capital Management) has no shortage of technology platforms, but Jerden makes a compelling case that providing managers with a tool that goes the extra mile to personalize the employee’s experience makes all the measurable difference toward organizational performance, retention, individual happiness, inclusion, and innovation.

Science4Hire is brought to you by Rocket-Hire - your partner for launching successful talent assessment strategies and solutions. For more information on Rocket-Hire, visit https://rocket-hire.com

Related Resources
Find Eddie Jerden on Linkedin
Learn more about Humu at https://humu.com
Follow Humu on Twitter - @humuinc

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In this episode, Dr. Charles Handler chats with fellow I/O Psychologist, Dr. Neil A. Morelli, VP of Product and Assessment Science for Berke. Neil specializes in talent acquisition, data science, and product development, but has a special affinity for entrepreneurs.

During the discussion, they dig into the comparisons that can be drawn between those who pursue a career in I/O psychology with those who have (what can best be described as) an “entrepreneurial spirit”. The most prevalent being the shared focus on innovation and creative thinking; and how experimentation and seeking solutions is a trait that has a great deal of overlap between the two pursuits.

Neil, who has been an active supporter of I/O psychology entrepreneurs, also extends an invitation to a special event taking place during SIOP 2020 - I/O Entrepreneurs Networking.

During I/O Entrepreneurs Networking (a new addition to the SIOP schedule), Morelli encourages anyone interested in I/O entrepreneurship, independent consultants (or aspiring ones), those with product ideas, or those interested in meeting others in the I/O space to meet like-minded professionals.

In addition to networking, the event will feature an informal showcase of I/O-created businesses. This “startup village” will allow interaction between those aspiring to build their own products and solutions with others who have or are in the process and willing to show their progress.

SIOP 2020 - I/O Entrepreneurs Networking Event Details

Date / Time: Thursday, April 23rd from 5:00 to 6:00 PM
Location: Room 305 of the JW Marriott (host hotel)
Interested in showcasing a product? A few spaces are still available.
Contact Neil via email at neil@berkeassessmnent.com to have your I/O product considered for the showcase.

The pair goes on to discuss the factors that are leading to the shift in more I/O’s embracing and implementing more entrepreneurial efforts to the benefit of the companies they work for (or to embark on their own endeavors).

Before joining Berke, Dr. Morelli served as the Head of Product and Selection Science for The Cole Group, an executive search firm serving high-growth technology companies. He has also worked for an early stage, web-based assessment company and as a talent management consultant for Fortune 500 companies including Georgia Pacific, Wal-Mart, Bridgestone Tires, and Texas Instruments. Dr. Morelli received his Ph.D. in I-O Psychology from the University of Georgia and his M.S. in I-O Psychology from the University of Tennessee at Chattanooga. His research interests include technology-enabled recruitment and assessment and he has authored several articles and book chapters in a variety of peer-reviewed publications.

Science4Hire is brought to you by Rocket-Hire - your partner for launching successful talent assessment strategies and solutions. For more information on Rocket-Hire, visit https://rocket-hire.com

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With over 660 million members, LinkedIn has access to the global workforce and related data like no other platform in history. The platform and its evolution have been a game-changer for both job seekers and talent acquisition professionals.

In this episode, Daniel Maurath, I/O psychologist, and Senior People Analytics Consultant at LinkedIn discusses LinkedIn’s core mission to “Create Economic Opportunity for all members of the global workforce” and how the data provided by members is core to the evolution of the platform. Maurath also speaks to the significant role data plays in identifying trends that provide LinkedIn with the insight needed to evolve their product offerings.

Daniel relates that the top 3 trends trends identified through member surveys and data include:
Low unemployment is resulting in a sellers market
Rapid skill changes are requiring reskilling at an increasing rate
Emergence of the “purpose economy” (where work is all about passion and purpose) resulting in a need for companies to change their approach.

We also discuss the launch of (and plans for) LinkedIn’s Skills Assessments in September 2019. This new offering provides individuals with an opportunity to showcase their skills to employers. This is LinkedIn’s first step in credentialing, but they have also highlighted this as an area of future growth - so stay tuned.

LinkedIn’s never ending quest to reduce the friction between job seekers and employers is core to this episode. Maurath provides listeners with a look inside the behemoth’s current and future efforts to invest in data, analysis and the people who understand both, and the impact this combination will have on the future of work.

Science4Hire is brought to you by Rocket-Hire - your partner for launching successful talent assessment strategies and solutions. For more information on how Rocket Hire, visit https://rocket-hire.com

RESOURCES

URL for Podcast Page on RH - https://rocket-hire.com/podcast/linked-into-the-workforce-i-o-psychologys-role-linkedin

Daniel’s LinkedIn Profile - https://www.linkedin.com/in/danielmaurath/
I/O Psychologist
Skills Assessment Launched - https://blog.linkedin.com/2019/september/17/announcing-skill-assessments-to-help-you-showcase-your-skills

How he went to work for LI?
Formerly Talent Insights, which became People Analytics
Data is one area (reporting, dashboarding)
Research area (bigger projects that drive major changes to the platform)
HR, Benefits, Compensation
Formerly worked with the Product Team to make better

LinkedIn
660 Million Members

Reed Hoffman Quote:
“Data only exists within the framework of a vision you’re building to, a hypothesis of where you’re moving to.”

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In this episode, Dr. Charles Handler chats with friend and colleague, Dr. Steven Hunt of SAP, an Industrial-Organizational psychologist and recognized expert on strategic human resources. Dr. Hunt has made a career focusing on the use of technology to create more effective workforces.

This episode explores the intersection between technology, human performance, and business performance. The conversation focuses on using technology to tie micro and macro-level data together in order to provide a more complete view of the value of humans to strategic, organization-level metrics.

Dr. Hunt explains that true shifts in business success requires understanding how workforce composition impacts organizational performance. Typically organizational performance is evaluated using data from key functions such as sales and marketing and does not give people data a seat at the table. Recent progress in technologies such as that provided by Dr. Hunt’s employer SAP, facilitate the integration of HR systems with business systems (such as those supporting sales, marketing, supply chain, etc.).

Hunt explains that tying people data to business data is the only way to elevate staffing to be more strategic. Systems that integrate HR data with business data will provide a critical foundation for the change by challenging leaders to think about people differently. Conversely- both Drs. Hunt and Handler agree that this shift will also require I/O and HR professionals to understand business data and how to mesh it with people data.

Dr. Hunt has over 25 years’ experience designing systems for a variety of human capital management applications including performance management, staffing, employee and leadership development, culture change, workforce analytics, and succession planning. He is also the author of two books on HR process design and implementation: 'Common-sense Talent Management: Using Strategic Human Resources to Increase Company Performance' and 'Hiring Success: The Art and Science of Staffing Assessment and Employee Selection.'

As always, this edition of Science4Hire is brought to you by Rocket-Hire!
Rocket Hire is your partner for launching successful talent assessment strategies and solutions. For more information on how Rocket Hire can help you and your organization, contact us to schedule a time to discuss your company's challenges.

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During this Science4-Hire episode, Dr. Charles Handler mashes up his rabid football fandom and his passion for employment testing to discuss the NFL’s Player Assessment Test (or NFL-PAT) with the I/O psychologists responsible for creating and maintaining it.

Implemented in 2013, the NFL-PAT is an hour-long, computer-based test measuring a wide range of traits and competencies directly related to a player’s on- and off-the-field performance.

Special guests and creators of the NFL-PAT, Drs. Harold Goldstein and Charles Scherbaum of Baruch College and Dr. Kenneth Yusko of the University of Maryland, provide a detailed rundown of the strategy behind this state of the art assessment, the work put into creating it as well as the incredible results it has delivered.

This discussion is the perfect blend of sports and science, offering loads of insight that will entertain any football fan while sharing general truths required for success with any talent assessment program.

Interesting points covered include:
-The historical, strategic factors behind the initiative
-The luxury of having access to a treasure trove of player data
-Expanding the job performance criteria to include off the field measures
-Creating specialized assessment content that is context-specific for football
-How the NFL actually uses the program and the value that it has created.

Finally, we discuss the success of the NFL-PAT assessment, and how other any company can leverage the best practices used by the NFL to up their hiring game.

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David Hain is the guest for this discussion of scaling and growth for the HR technology and talent assessment industry. Mr. Hain currently serves as the Managing Partner of Gotham Growth Group, and previously served as the Chief Executive Officer and Chairman of the Board of HR Technologies, Inc.

Mr. Hain is a highly energetic and successful business leader with diverse experience driving profitable growth in challenging, competitive, and volatile customer markets. His firm provides outsourced corporate development all focused on growth and providing clients the ability to allow clients to leverage existing investments in intellectual property, technology and channels of distribution into rapid, profitable, and sustainable growth in new or existing markets.

Highlights from this episode include:

A discussion of how investors view the assessment space
Figuring out how to make assessment a viable commodity
The need for talent assessment technology to straddle science and the business of technology to create solutions that companies need.
What companies are doing to overcome the issue of the fragmented candidate journey.
The trick to scaling assessments

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Dr. Charles Handler is his own featured guest this week, as he gives us his own professional background before expounding on what he sees coming for next year.
Dr. Handler discusses his take on important trends by answering a series of questions, including:

1. What is the biggest shift we are seeing in employment testing as we move into 2020?

2. What concerns Dr. Handler most in 2020?

3. What do consumers of talent assessments really want?

4. What will 2020 bring in terms of AI based assessments?

5. What is the business outlook for talent assessments in 2020?

6. What would Dr. Handler’s advice to a company looking to add talent assessment to it’s hiring workflow in 2020?

As always, this edition of Science4Hire is brought to you by Rocket-Hire!
Rocket Hire is your partner for launching successful talent assessment strategies and solutions. For more information on how Rocket Hire can help you and your organization start 2020 on the right foot, visit our site: https://rocket-hire.com

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This episode takes the listener on a journey through some fascinating topics- All of which tie back to human nature and the world of work. Our guest star is Tomas Chamorro-Premuzic an organizational psychologist who is currently a professor of business psychology at University College London (UCL) and Columbia University, as well as the Chief Talent Scientist at ManpowerGroup.
We discuss Tomas’ book "Why Do So Many Incompetent Men Become Leaders? (And How to Fix It)." from the perspective of humans and their relationship to work and organizations. It is clear that the world of work represents a complex dynamic between individuals and organizations. When looked at from the dark side- this interplay can lead to waste in the form of unhappiness and poor economics. This dialogue provides a really interesting foundation for a subsequent discussion of the future of assessment.
The focus turns to assessment at scale and the many issues that play into a future where personal data provides trait based insights without the use of a traditional assessment. A focus on the economics of assessment at scale provides some unique and fascinating insight into the future of assessment as a discipline AND a business.
At the end of the day- the hidden treasure in accurate, ethical assessment at scale lies in the ability to increase productivity through helping humans find jobs that align with their talents and interests.
At scale such matches will provide new levels of personal and economic enlightenment and drive a new age of prosperity!

About Tomas Chamorro-Premuzic
Tomas is one of the founders of Meta, a company that creates data-driven tools to help corporations identify employees and leaders with entrepreneurial talent.
He also serves as a consultant for both the private and public sectors, with clients that have included JP Morgan, Goldman Sachs, HSBC, Prudential, Unilever, the British Army, the BBC, Twitter, Spotify, and Harvard’s Entrepreneurial Finance Lab. He is the director of the MSc in Industrial-Organizational and Business Psychology at the University College London (UCL), having previously co-run the MSc in Occupational Psychology at Goldsmiths, University of London. He has also previously taught at the London School of Economics and New York University. He is the author of the column Mr. Personality, which appears regularly on Psychology Today, and contributes regularly to the Guardian, Fast Company, Management Today, Forbes, and Harvard Business Review. In February 2015, Dr. Chamorro-Premuzic assumed the role of CEO at Hogan Assessments after spending two years as the company's vice president of research and innovation. The company provides personality assessment services that corporations can utilize for hiring and training purposes.

Find the original article, Tomas' book is based on here: https://hbr.org/2013/08/why-do-so-many-incompetent-men
And grab a copy of the book yourself here:
https://www.amazon.com/Why-Many-Incompetent-Become-Leaders/dp/1633696324

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Can you "teach" consulting?
How do the masters learn?
What are the universal truths and secrets to success?

These are the fundamental question that Dr. Van Latham answers with Dr. Charles Handler in this episode.

There has traditionally been no real training for consulting skills. Most of us have learned via experience and many mistakes! There are some central themes that represent tricks of the trade.
In this episode, two highly experienced consultants offer words of wisdom to those interested in building consulting careers.
Dr. Latham also shares information about the consulting program he teaches at the University of Texas at Dallas.
This episode is a must listen for anyone who is looking to up their game when solving problems for clients.

Van M. Latham is an Industrial/Organizational psychologist specializing in Human Resources management. Dr. Latham works in all areas of Human Capital, but is known primarily for his work in organizational and leadership development. At PathPoint, he has helped businesses improve organizational capability and performance through effective people practices. He has consulted with some of the world's most recognizable companies and brands, such as American Express, AMD, Ashland, Biogen Idec, CVS/Caremark, Dollar General, Ernst & Young, Foot Locker, Harvard University, Hershey's, Hubbell, Lenovo, McGraw Hill Financial, Marriott, Paychex, PepsiCo, SAP, and Thermo Fisher Scientific.
From the launch of his own consulting practice to his work with the University of Texas at Dallas as part of their Organizational Consulting Certificate program, Dr. Latham is the dynamic personality with a wealth of experience to share with listeners, no matter where they are in their own consulting journey.
Find more about Dr. Latham and his work at these links:
https://pathpointconsulting.com/
https://obcc.utdallas.edu/organizational-consulting-certificate/

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This episode’s guest is Kevin Wheeler, Founder, and Chairman of the Future of Talent Institute. Kevin is a futurist and a true thought leader whose personal brand brings to the party a practical but future-forward blend of wisdom.

Kevin brings a really strong foundation in understanding the role talent will play in shaping the future business landscape. It is through the lens of a hybrid futurist- realist that Kevin discusses his views on how assessment fits into the big picture.

In this episode, Kevin shares his views on the state of recruiting and talent assessments 10 years from now.

According to Kevin, there is a utopian view that AI will take over the need for human intervention in hiring. His view does not align with this. Rather Kevin feels that 10 years the view will not be radically different than it is today. Instead we will see a more evolutionary change that will be subtle and consist of augmentation as opposed to redefinition.

Kevin feels that 10 years from now there is no way we will not be able to push a button and deliver the perfect candidate. Instead, using systems to quickly settle on a shorter list of candidates that are better qualified is more realistic. In fact, the entire hiring experience will be streamlined to be simpler and easier- with administrative tasks being handled by the machines freeing up humans to do what they do best.

I asked Kevin, “How his clients feel about the AI based TA tools available today? Are they afraid or excited?” Kevin’s reports that his clients are confused, skeptical and scared. They do not know if the technology that is being advertised is for real. They also fear that tech tech may take their jobs someday. Most recruiters don’t trust the black box of AI. They need better education about what the tech is really doing.

When it comes to the practicality of advanced tech’s role in the hiring workflow, Kevin’s clients generally show that we have a long way to go. This is because, despite the hype, the reality is that even though there are some cool tools out there, they still have to exist within a big-picture process and workflow. Typically getting new tools to play well with existing legacy systems is an exercise in frustration. Putting an exciting tool into a process that is not its equal can create a log jam that keeps the value of the new tech from being realized.

According to Kevin- There are 3 key areas where AI is going to lead change over the next 10 years

Sourcing- Finding candidates will be much easier via the assistance of AI. We already see this trend, but in the next 10 years, the sourcing function will become both more automated and more accurate.

Assessments- Humans are complex and assessments don't tell the whole story. We are a long way from being assessed by AI alone. Insights can be delivered via AI and the most basic level can be automated, but blindly expecting AI to radically change assessments in 10 years is foolish. The biggest change will be the increased use of work samples that are augmented by AI.

Chatbots- Chatbots and assistants in the here and now are not really too capable, in 10 years we will have very capable assistants to help us. This will mean that applying for a job will be a much better experience for candidates. We all want a good human experience. By removing the admin layer AI can help stuff will free up TA to provide the human experience candidates want.

Yes, we will still have assessments in 2028, they will be easier to use and will provide a smoother, more realistic experience for candidates and TA. But beware the danger of over-relying on AI to come rescue us. Kevin is confident that we won’t be getting a magic button to deliver the perfect employee anytime soon.

Kevin leaves us with the ideal strategy for the next 10 years (and beyond)- “Question everything!”

Kevin Wheeler is the Kevin started FOTI out of his passionate belief that organizations need a more powerful and thoughtful architecture for talent than they have at present. After a 25 year career in corporate America serving as the Senior Vice President for Staffing and Workforce Development at the Charles Schwab Corporation, the Vice President of Human Resources for Alphatec Electronics, Inc. in Thailand, and in a variety of human resources roles at National Semiconductor Corporation, Kevin has firsthand knowledge of the need for better strategies and approaches to finding, developing and retaining people.

Kevin is a globally known speaker, author, teacher and consultant in human capital acquisition and development, as well as in corporate education. He is the author of numerous articles on human resource development, career development, recruiting, and on establishing corporate universities. He is a frequent speaker at conferences. He writes a weekly Internet column on recruiting and staffing, which can be found at www.ere.net, and he and Eileen have written a book on corporate universities, The Corporate University Workbook: Launching the 21st Century Learning Organization. He has served as adjunct faculty at San Jose State University, the University of San Francisco and on the business faculty at San Francisco State University.

Learn more about Kevin's work at https://futureoftalent.org or email Kevin at kwheeler(at)futureoftalent.org

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In this episode Dr. Handler & Dr. Fred Oswald- past president of SIOP and scientist extraordinaire discuss a range of topics related to the past, present, and future of employment testing.
The discussion starts with the foundational reasons employment testing exists- immutable things such as the value of measuring individual differences. The discussion then turns to innovation in the field of personnel selection and the application of new concepts shaping the future of testing such as the “open science” movement.
Dr. Oswald shares his thoughts about who will shape the future of testing- will it be applicants’ demands for a consumer grade experience when applying for a job, technology companies, or I/O psychologists?
Finally- Dr. Oswald shares details of the research he is currently conducting.

Dr. Fred Oswald is a Professor in the Industrial/Organizational Psychology program within the Department of Psychology at Rice University. He recently served as the president of the Society for Industrial and Organizational Psychology (SIOP). With more than 8,000 average annual members, SIOP is the world’s largest professional organization for industrial and organizational psychologists. His expertise deals with personnel selection and psychological testing in organizational, education and military settings.

Dr. Oswald’s work deals with defining, modeling and predicting societally relevant outcomes from psychological measures that are based on cognitive and motivational constructs. The conversation ranges across both Dr. Oswald's predictions, but also the state of the industry today.
Learn more about Dr. Oswald and his work at http://workforce.rice.edu or email him at foswald(at)rice.edu

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This episode looks at assessments from the perspective of a talent acquisition leader and asks the question-- Do talent acquisition leaders really care about assessments?
http://talentgrowthadvisors.com/

According to Linda- understanding the space given to assessments by talent leaders requires that one really understands what keeps them up at night.

Unfortunately, the answer to this question is not a simple one. Pain points in the hiring process are often the result of many factors converging in a “perfect storm”. Placing all the blame on one component (i.e., ATS, assessment, background check, etc.) will not fully remove the pain. In many cases the TA process and workflow is like Frankenstein’s monster- a bunch of stitched together parts moving awkwardly together.

Fixing challenges faced by TA leaders requires a holistic evaluation that applies a supply chain mentality to the entire process and its components. When one considers all of the various moving parts that are needed to create a successful hiring process- assessments far from the first thing that comes to mind.

To succeed with an assessment program- one has to embrace the fact that a good hiring process is much bigger than just putting a good assessment in place. So what part of the big picture is giving talent leaders fits?

According to Linda- it starts with our current labor market. It is a candidate’s market right now and the simple fact is that the supply of talent cannot keep up with demand. This is the lens through which a talent leader sees assessments.

With candidates in short supply- you have to make sure you are attracting talent, not repelling it. This calls for a focus on brand management. The standard method for recruitment brand management has been an employment page with stock photos and videos that all end up looking the same.

In today’s market - a targeted, differentiated approach to branding is a must. Talent leaders be creative and resourceful enough to find ways to target ideal candidates and surprise and delight them. Every decision about what goes into the process and how it is packaged is a balancing act that requires leaders to make decisions that will not upset and repel prospective applicants.

This message sounds like really bad news for those pushing for assessments, but it is far from a death knell. One just has to respect the fact that he labor market shapes the box that assessments must fit in when it comes to engaging talent leaders. Any assessments used must not disrupt the apple cart when it comes to attracting talent and compelling them to complete your application process.

So what are the rules exactly?

From an operational standpoint- the assessment has to fit a supply chain mentality. It must be tightly integrated into the process and the right data must be delivered to the right place at the right time.

From a candidate perspective - there must be a compelling reason for the assessment, the assessment must be short, and it should speak to the uniqueness of the employer’s brand.

Even meeting the above criteria does not guarantee the acceptance of an assessment into the workflow. Ultimately- it boils down to the role itself, what the competition is doing, and the difficulty faced in filling openings.

Assessments definitely have a fighting chance in today's market- but require a realistic view on the context in which they must work and the ability to adapt to its constraints.

Linda Brenner is the Managing Director and Founder of Talent Growth Advisors, based in Atlanta. She’s passionate about defining what “good” looks like and taking concrete, measurable and staged steps to get there.

Find Linda and her organization at https://www.linkedin.com/company/talent-growth-advisors or at https://talentgrowthadvisors.com

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This episode explores the various ways social media information is being used to evaluate job applicants and provides expert opinions on their viability and general acceptance. Special guest Dr. Shawn Bergman’s expertise in the area of social media for hiring, as well as his own research provide a great backdrop for filtering general perceptions about this hot topic. The conclusions drawn provide a useful context for anyone interested in exploring the use of social media data as an assessment tool.
Before looking at the various ways social data is used in hiring, it is important to look at the some of the issues that apply to using social media data to make employment decisions.
Concerns
There are four overall areas of concern when it comes to using social data as an assessment.
1. Problems with source materials- There are a great deal of individual differences in the use of social media. For instance, both Dr. Bergman and Dr. Handler admit that they are not very active on social platforms. It seems logical that a lack of data based on infrequent posting or not using a particular channel- could have a negative impact on an applicant’s evaluation. The potential differences in how much people post and where- represent a challenge when comparing them to others who may post frequently to many channels.

1. Accuracy- The accuracy of social media data in measuring human traits is speculative at best. Without confidence in construct related measures- the value of social data as a predictor of job performance is suspect. Of course it is possible to simply correlate patterns extracted from social data with performance metrics- but this brings us right back to the same issues raised with most AI based evaluation tools. Unfortunately, many companies making social media assessment tools do not involve I/O psychologists - adding additional concerns when it comes to the measurement of actual job related constructs.

2. Privacy- The default assumption with social data in hiring is that the individual being evaluated has not given specific consent for their information to be used. This is probably the most talked about issue when it comes to social media data for hiring, and one that will not go away anytime soon. One of the main problems in the realm of privacy is that it is often impossible for an individual to know their data is being used to evaluate them. While opting in is becoming a standard requirement- it is virtually impossible to police the use of tools that harvest and evaluate social media data. There is also the question of who owns social data. Questions of data ownership can turn the concept of privacy on its ear- requiring legal precedent and legislation to sort out.

3. Bias- There are several ways bias can enter the room when it comes to hiring and social media data. Any process in which profiles are reviewed manually presents a serious snakepit when it comes to bias. Automated tools are also famous for creating systematic biases when making evaluations. While there is some great work being done to train AI/ML to actually reduce bias, the fact remains that social media data helps keep the very real issue of bias alive and well.

Use Cases
Dr. Bergman reports that 70% of organizations use some form of social or public data when evaluating applicants. So how are they using these tools?
Manual review of profiles/posts
While the sexiest and most talked about use case centers around the use of AI based tools to systematically evaluate data from social media accounts and posts, the lowest hanging fruit when it comes to the use of social data for hiring decisions is the simple review of profiles by humans. Social media data is often used for a very low tech and manualized process of reviewing profiles of job candidates to look for inappropriate information, revealing posts, etc. This process is accessible to anyone with a computer, setting up many a disaster when it comes to subjectivity, accusations of immorality, etc.
Human review of profiles for problematic behavior definitely opens up a great deal of concern. It is accessible and there is no accountability for reporting the results of these often ad hoc evaluations. While these evaluations can be outsourced to firms that specialize in the evaluation and return a report, this does not legitimize the method.
The available research in this area shows that there is no relation between these evaluations and performance on the job. Furthermore, this type of easily disadvantages protected classes, and its job relevance is often hard to demonstrate.

AI based tools
The core of all these tools is tech that spiders the web to find profiles and information, scrapes the data, and then interprets and processes it into an output that can be used to support decision making.
The tech can work passively- based on open web searches with no opt ins, or more actively - with applicants opting in to share information that can be used to evaluate the applicant.
Social media data is often used as part of the sourcing process. The most common use case of technology enabled tools is passive sourcing in which social data is used to identify persons who may be a good fit for a particular job so that they can be contacted about an opportunity. This use case presents a number of difficulties because it happens outside of the actual application process. Individuals may not know their data is being used and the tools used to harvest data may do so in a biased manner. These methods are not actually assessments if they aren’t part of the formal hiring process. However, there is accountability in record keeping when it comes to sourcing efforts.
Social media data is also used to create a “super profile” that can be used for hiring. Profiles constructed using social media data most commonly package the data into a personality profile. There are currently many different tools that allow anyone to try creating a personality profile from their social accounts. Both Drs. Handler and Bergman report that when they tried out these tools- the results did not seem accurate.
Research so far has not shown there to be any strong correlation between machine derived personality profiles and those of more traditional methods. These tools have also not shown any real correlations with job performance.
The future is bright
The consensus from the research and experts in the area of social media based assessment is that “we are not there yet”.
As with all advanced technology tools, we can expect to see much improvement in the future. While the technology side of things will definitely advance our ability to make meaningful predictions from social data- the moral and ethical boundaries surrounding the use of these tools will likely remain.
Looking to the future it is important that we understand that technology is a tool, not a solution. When this approach is taken- the tools of the future will be less haphazard and more systematic in nature.
The net of it all is that there are exciting times ahead in the realm of social data and predictive hiring. But getting there is going to require an interdisciplinary approach that recognizes legal and ethical boundaries.

Shawn Bergman is a Professor of Industrial-Organizational Psychology and Human Resource Management at Appalachian State University, focusing on organizational systems, soft-skills training, leadership development, and the application of technology and analytics to solve applied problems.
Dr. Bergman has been inducted into the Appalachian State University Faculty Hall of Fame and received numerous teaching honors and awards, including a Board of Governors Excellence in Teaching Award. He is also the co-founder and Director of Research and Evaluation at the Vela Institute.
Learn more about Shawn and the Vela Institute here: https://velainstitute.org/our-team/shawn-bergman/
See what Appalachian State is building here:
https://iohrm.appstate.edu

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In this episode listeners are treated to 30 minutes of storytelling and inspiration from our special guest Mark Newman, Managing Partner of Liam June Ventures, a prolific investor and founder of pioneering video interviewing company, HireVue.

As a founder of HireVue, a trailblazer in video interviewing and video assessment, Mark has been at the intersection of hiring and technology for most of his entire career.

The episode kicks off with the fascinating story of HireVue's inception and early days and moves on to discuss the role of advanced tech in shaping the future of I/O psychology and selection science.

The entire conversation is inspiring. From Mark’s perspective creating the future is about embracing change and maintaining a long term focus while being patient enough to accept the limitations of the moment.

Mark brims with optimism about the future of selection science and assessments, stating that..

"From an I/O perspective, we're entering into a golden age of the principles of I/O Psychology being applied in ways that no one that entered the field, 10, 20, 50 years ago every imagined."

The conversation then turns to the journey.

We are still in a time when AI based assessment is not appealing to every employer. Finding the future is about progressive employers who are willing to team up with good scientists to thoughtfully test tools and technology.

Mark’s take on the bias question is to remind us not to be scared of the present state, to understand that current tools have bias and that the desire to remove it will ensure that technology is managed to this end. It is important to understand that the careful oversight and expertise of psychologists exert a powerful directional force on the management of unintended consequences from AI based hiring tools.

In the end we are still in the larval stages of building the organizations of the future. These organizations will use I/O psych and data science at scale to ensure hiring is inclusive - allowing people an easier path to the job of their dreams.

For now- there is still lots of work to be done. When it comes to creating the future- perhaps the hardest work for many will be checking their fear at the door.

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While the EEOC’s Uniform Guidelines on Employee Selection Procedures (aka UGES) provide a solid foundation when it comes to the do’s and don'ts of employment testing- it is always nice to hear about the EEOC’s stance on key issues straight from the horse’s mouth.

In this episode special guest Dr. Romella El Kharzazi, of the Equal Employment Opportunity Commission teaches us about the EEOC’s mission and their stance on all things employment testing. This enlightening conversation also includes a good deal of talk about the agency’s stance on AI based assessment tools.

This episode is a must listen for anyone who is considering using AI based assessment tools but is concerned about the legal risk.

Here is a quick outline of the key messages that arose during the conversation.

Getting to know the EEOC
The EEOC sees themselves as “ A force for good”- focuses on the core mission of fighting discrimination and making sure that everyone has an equal shot when it comes to employment.
The EEOC’s priorities are:
to protect vulnerable populations,
to educate businesses on discrimination and how to avoid it, and
to “build opportunities” for everyone no matter who they are or where they come from

The EEOC is not just about enforcement- they also create policy and do research into all areas of employment discrimination with a special focus on monitoring the employment climate at all federal agencies.

The EEOC and pre-hire testing
When it comes to testing, the EEOC’s core focus is on working with employers to monitor, manage, and eliminate bias from the hiring process. This is done via a formal complaints and investigation process that is geared towards working with employers to eliminate bias more than it is towards dragging them into court.

The requirements for compliance in hiring and testing are laid down by the UGES. The good news is that the UGES has been around a long time and legal and I/O Psych fields know them well and have been building testing products and programs that are compliant.

Beyond the specifics of the UGES- the advice for staying on the EEOC’s good side includes:

Job analysis- Establishing job relatedness of selection procedures is critical in all situations and should not be ignored. Especially when it comes to AI based tools, the absence of a job analysis will create exposure and risk.

Record keeping- Companies are on the hook for keeping applicant records- with no exceptions. It is not acceptable to claim exemption from the rules because your firm does not keep any records. While it is not mandatory for applicants to supply demographic data, compliance requires that there is a place in the application process for applicants to provide it. Failure to attempt to collect applicant demographic data creates exposure and risk

Be proactive- It is always required that employers seek alternative measures to replace those that may have adverse impact. Claiming business necessity for tests that show adverse impact only goes so far. Instead - it is your obligation to search for alternatives that have less adverse impact. Employers should be proactive and when aware of a problem- seek to fix it instead of keeping status quo and hoping they are not challenged.

AI based tools
While the EEOC does not yet have a big data policy, usual rules apply to AI based tools. It is really important to understand that the EEOC does not feel that all AI based tools have no merit. As long as the development, calibration, and use of the tools meet the requirements of the UGES, all is good. While there has yet to be a case related to the use of AI based assessments- the EEOC is paying attention and is active in monitoring how these tools are used.

So, how do we leverage the benefit of AI based tools without increasing exposure and risk?

First and foremost - there is a need to be sure that companies building and using these tools create a seat at the table for persons who understand the ins and outs of employment testing. Companies who rely purely on data science and empirically driven relationships are at risk. Adding I/O psychologists to the mix makes a ton of sense both for ensuring compliance and helping ensure the human side of hiring is properly represented. A hiring assessment company with no I/O on staff immediately sends up a red flag.

I/Os can help ensure that employers don’t overfit models to data sets, creating unreliable prediction across locations and over time.

It is paramount that employers do not simply push the blame for bias associated with a specific tool. Humans program and train AI based tools and it is people who make the ultimate decision to hire an applicant or reject them.

Bias can easily enter the equation when unsupervised learning is used so there is a need for caution when using these tools. For instance features such as social media data can be chock full of landmines that are biased against protected classes such as zip code and consumer behaviors. It is really important not to confuse the use of AI for customer/consumer insights from AI uses for hiring insights. They are not the same and what is valuable for understanding consumer needs is not appropriate or equivalent to what has merit when it comes to making hiring decisions.

If bias is present even with a strong correlation between the predictor and job performance there is still a great deal of exposure. In such cases the employer will be on the hook to:
Prove the assessment is job related- via a job analysis
Show that there is a business necessity for the bias, And
Show that alternative predictors were considered

If you are considering using AI based assessment tools for hiring here are the things that should guide your efforts:

Don't try to use advanced assessment tools based on a fear of missing out or just because it seems trendy. Make sure you have real business need.
Remember the adage - Garbage In, Garbage Out. Your models are only as good as the data you feed them.
Job Analysis is critical!!! It should be the starting point of all predictive hiring tools even advanced ones.
Include an I/O psychologist who is an expert in the UGES in the mix.

Follow Romella on Twitter at: https://twitter.com/RomellaJanene

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Dr. Ed Levine, Professor Emeritus in Industrial/Organizational Psychology at the University of South Florida, and a long time friend of Dr. Charles Handler joins us in this episode.

Dr. Levine literally wrote the book on job analysis. In this episode Dr. Levine shares decades of wisdom about the how and why of job analysis. The conversation takes an existential turn and provides really refreshing perspective about the true essence and value of job analysis.

So what is job analysis anyway and why is it so important?

To answer this questions Episode 4 includes a great discussion about the key inputs and outputs of job analysis, but then reaches into a refreshing philosophical territory.

Points of interest in the discussion include:

Essentially job analysis translates what people do on a job into words. While job analysis may seem cold and boring on the surface, it is actually a spiritual thing in many ways because it involves a search for meaning within the work people do.

Job analysis highlights the attributes needed for success at a job- providing a measuring stick for job applicants. The ability to hire based on the fit between applicants’ match to the human traits required for job success is of course critical to business outcomes. But job analysis has even more value because it helps individuals find work that is meaningful to them- therefore offering a larger benefit to society.

The entire concept of a job is now getting outmoded so the view is turning to “work analysis” this expands the domain to include emotions, teams, personality factors, etc. This covers the work process not just a job.

The future of job analysis is really bright. Beyond the applications for selecting employees, job analysis will continue to be a foundational aspect of the relation between humans and work. As the concept of a job changes, so too will job analysis.

We can expect to see the scope expanded beyond just the study of one person’s role in one discreet job. Instead we can expect to see job analysis extending into “work analysis” that transcends the walls of one organization. Work analysis will focus on the study of the things required for various types of work.

As work continues to become more team based in nature, we can also expect job analysis to be applied to the study of what teams need to successfully accomplish valued objectives. New technologies such as sensors and IOT devices will allow us a much deeper view on how teams interact in the workplace. Insights from this work will help promote a better understanding of the traits required to optimize the human elements that impact the success of work teams.

We can expect to see advances in technology contribute to the evolution of job analysis. In the future we can expect that job analysts will have access to database that contain the results of thousands of job analysis studies. Think of an evolution of the O*net system to include access to accumulated job studies will allow analysts to instantly dial up job profiles that are fairly complete. Imagine this data being yoked to AI based systems that provide a deeper level of intelligence about the human traits required for success.

Job analysis is a foundational part of any talent assessment program. Building an assessment without a job analysis is like building a house without a blueprint. But as we see in this episode, job analysis has a lot more to offer.
All in all- Job analysis will continue to be a bright star in the understanding of humans and their work.

Dr. Levine is a prolific author, speaker and consultant, working with governmental agencies, across the private sector and most recently with NASA on Astronaut selection. You can learn more about Ed by visiting his site: http://luna.cas.usf.edu/~elevine/

And watch for the 3rd Edition of his book, "Job and Work Analysis" here: http://sk.sagepub.com/books/job-and-work-analysis

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Ben Hawkes is an I/O psychologist who is a thought leader in area of game based pre-employment assessment tools. Ben is currently the co-Founder of BlackHawke Behavior Science and has served as the Selection Assessment Lead for Shell International since 2016.

Ben’s background and experience make him a perfect resource for helping Dr. Handler answer the question,

"Are game-based assessments the future of employment testing?"

The short answer as revealed in this episode is: Don’t believe the hype!

Game based assessments have a ton of value and are are PART of the future, but they have the same limitations as traditional assessments (if not more). Game based assessments will make a positive and essential contribution to the future of assessments because they will help us learn how to make assessments more lifelike and enjoyable.

This episode touches on few key points to help educate and orient those interested in game based assessments

1. Gamified assessments are not true games

It is important to know what you are looking at. It is important to be careful not to believe the hype that a gamified assessment is really a game. Doing so is made harder because it is difficult to say exactly what is a game.

Games have to meet certain requirements such as the presence of rules, an enjoyment factor, suspense, and control by the player. But there are grey areas around the true definition and it is common for things that have game like elements to be called games when they really aren’t.

Gamified assessments- that is assessments with game like elements, are becoming increasingly common and are more likely to be an integral part of the future of assessment.

Gamification has many advantages because they make assessments more enjoyable but allow them to maintain the characteristics of a good measurement tool. This is essential because when all the dressing is stripped away- assessments must be psychometrically sound measurement devices.

Assessments that provide gamified elements such as: feedback, choices, dynamic user interfaces, and realistic environments definitely have a lot going for them. The upgraded user experience provided by gamification means we can expect to see an increase in their use. Just know they are not truly games and that gamification alone is not enough to make an assessment legit.

1. Where does the risk lie?

A good rule of thumb when looking at game based assessments is don’t buy based on the sizzle because the steak may taste like crap.

For those shopping they first need to know that to be usable as an assessment a game must meet the minimum standards of being fair, reliable, and valid. Many tools may look like or be called an assessment but may not fit these minimum requirements.

One type of game that is commonly mistaken for real assessment is the “attraction games”. These are branded experiences that allow the job seeker to interact with a job or organization. They are great for employment branding but typically are not designed to be measurement tools.

Another type of game that must be approached with caution are “non-contextualized” games. These games take place in a simulated environment such as outer space or under the ocean. While these are sold as being attractive to applicants they may actually have the opposite effect.

Candidates actually value assessments that appear job relevant more than they value being entertained by the experience. In the world of candidate experience, fairness is king and candidates value job relevance over fun.

Personality tests alone, be they games or regular measures, are not strong predictors of work performance. So it is not a surprise that personality games have struggled to be effective as selection tools and their ability as strong predictors of applicant performance should be met with a healthy dose of skepticism. Yes, it is possible for games to measure aspects of personality but they have not proven to do so with at the accuracy level of more traditional assessments.

Many games, including those that claim to measure personality, are sold based on the number of data points they generate about an applicant. These data points are called “paradata” and they are the byproduct of all the actions within the gaming experience. The sheer number of data points does not mean an assessment is a good measurement tool. Measuring constructs that underlie work performance requires understanding what you are measuring first and foremost and then creating accurate and reliable measurement tools. Just because you have millions of data points does not mean the patterns that belie personality are found within it.

Finally- games may not meet accessibility standards required of selection tools. It is important to fully evaluate any assessment games for compliance to ADA standards.

1. So what can we trust?

Gamified assessments are great as long as they are held to the same standards as regular assessments. Currently the strongest type of assessment games are cognitive games. These games have many advantages over regular cognitive assessments they are more engaging, work better than traditional assessments on mobile devices and early research shows that they actually work better than regular cognitive assessments while having less adverse impact.

Cognitive games are also very good at measuring multi-tasking which is becoming increasingly important for many jobs.

3a. We can trust that games will be an important part of the future of assessments

In the future games will help measure things that are not easily measured by static assessments. We can expect to see an increase in the use of gamification techniques to make games increasingly more enjoyable. We can also expect to see games become more like simulations that mimic the real workplace. We can also expect to see a modular approach where many mini games that measure specific traits are packaged together in a branded wrapper. We can also expect to see more Virtual and Augmented reality based assessment games.

Are games the future? They are definitely part of it as they offer many attractive elements that will help talent assessments evolve.

Visit www.blackhawke.io to learn more about Ben and his work supporting venture capitalists (VC) and other investors by analyzing the psychological strengths and weaknesses of entrepreneurs and startup teams. BlackHawke equip VCs with insights and guidance to minimize conflict, address the inevitable 'people issues', and accelerate startup growth. And connect with Ben on LinkedIn: https://www.linkedin.com/in/benhawkes/ or Twitter: https://twitter.com/WorkPsy or even email him at Ben(at)yacmo.com

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There is no one better to weigh in on this most timely of topics than Gerry Crispin, co-founder of CareerXroads and the TalentBoard and long time champion of those who demand more from their hiring experience.

In this episode provides an honest answer to the question:
"Can assessments and a positive candidate experience peacefully coexist?"
Gerry makes the answer simple: Candidates do not hate assessments at all. What they hate is engaging in a hiring process that seems unfair. Perceptions of fairness are driven by many things- where the assessment falls in the talent acquisition workflow, selling the value of the assessment to the candidate, and feedback from recruiters.

But the #1 factor in job candidate’s positive feelings about an assessment is a clear link between the content of the assessment and the job they are applying to (aka “face validity”). The idea that candidates drop out of the process due to the length of an assessment is a myth. Candidates disengage from the process when they feel their time is being wasted by irrelevant questions delivered in what appears to be a vacuum.

Beyond this revelation, Gerry provides some great ideas for those who want to make pre-hire assessments a value add to the candidate experience and delivers the good news that they can actually play a starring role.

You can follow Gerry on Twitter at https://twitter.com/gerrycrispin
Connect on LinkedIn here: https://www.linkedin.com/in/gerrycrispin/
And Learn more about CareerXroads here: https://cxr.works

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Dr. Richard N. Landers is the John P. Campbell Distinguished Professor of Industrial-Organizational Psychology at the University of Minnesota, and directs the TNTLAB (Testing New Technologies in Learning, Assessment and Behavior). Dr. Lander’s is one of the foremost authorities on advanced assessment methods. His research on advanced gamification, AI, and Virtual reality based assessments techniques make him the perfect person to answer the question,

"Are advanced assessment technologies overhyped in 2018?"

This episode’s conversation seeks separate fact from fiction when it comes to AI based pre-hire assessment tools. In discussing the current state of advanced assessment techniques- consideration is paid to both the benefits and drawback of techniques that are being hyped as revolutionary (e.g., Natural Language Processing, Machine Learning, Gamification, and Virtual Reality).

Dr. Landers shares his view that these tools are still in their early days and a rush to widespread application over more trusted methods may be premature. Dr. Landers also highlights bright spots in the current state of advanced assessment tools and provides suggestions for getting the most out of them.

Through TNTLAB, Dr. Landers trains Ph.D. students and undergraduates in this research area and I/O psychology more broadly. If you are interested in becoming a part of TNTLAB in either of these roles, please see this page http://rlanders.net/join/. If you are interested in learning more about TNTLAB research, please see their Research Areas to learn more: http://rlanders.net/research/

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Coming December 2018, Science 4-Hire with Dr. Charles Handler.

The first and only podcast about everything talent assessment! A new episode featuring experts in the field discussing the latest developments in hiring every two weeks where ever you find Podcasts and always at Rocket-Hire.com