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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A Map to Navigate AI Governance, published by Nicolas on February 14, 2022 on The Effective Altruism Forum. Summary: In this post, we aim to disentangle AI governance. To do so, we list and explain 17 common activities in AI governance, including typical actors, typical outputs, and examples of actions and actors underlying each activity. We describe how these activities relate to each other through three governance pathways or “macro-activities”: Hard Governance, Industry-wide Self Governance and Company Self Governance. Hard Governance refers to government’s lawmaking, which are translated into tech companies corporate policies and internal controls, which ultimately affects AI labs managers and developers. Industry-wide Self Governance refers to industry players forming a coalition to adopt new norms or standards that are then translated into companies policies, where they affect AI development. Company Self Governance relies on internal coalition-building to alter the corporate policies, which in turn affects the AI lab. Each of the three “macro-activities” is composed of some of the 17 activities we describe. We aspire to capture all common AI governance activities, from researching macro strategy and applied research all the way to enforcement of regulations and policy evaluation. However, we identify five macro-activities for which we either have too limited knowledge or find them too specific to explain meaningfully in this overview: Military & National Security governance, Supply Chain & Trade governance, Multilateral soft governance, Extralegal governance, and Academic governance. These would benefit from further research. Finally, we list several questions for future research in the EA community. Acknowledgments: Thanks to Tim Fist, Aaron Gertler, Gabriella Overodder, as well as four anonymous commenters working in policy and politics, for providing feedback on various versions of this work. How will this post be useful to you? You can use it to start disentangling the AI governance process and as an overview of the main activities and types of actors in the space, and of how they functionally relate to each other. You can “zoom in” on an activity you’re especially interested in by going straight to it using the table of content (on the left on desktop browsing) or skim several activities when you are trying to understand an actor’s function or its position in the ecosystem. You and others can use it as a common conceptual framework to facilitate effective actions, as well as coordination and communication. You can use it to enrich the assessment of your career options and to refine your own theory of change. You can use it in conjunction with the longtermist AI governance map to identify gaps in longtermist AI governance activities. How can you help? This post is a first step to understand the map of the AI governance territory. You can contribute to this framework by answering these three questions: What are the activities we have missed? How would you describe them? What are the nuances we have missed in describing these activities? How would you break down even further these activities? What other projects could help answer the research questions highlighted in the section “Further Research Questions” at the bottom of this post? Epistemic status & other disclaimers: This post is based on our and our colleagues’ experience in AI governance. That experience is mostly derived from work in the EU, the US, the OECD, France, and the G7’s Global Partnership on AI, and from interactions with AI developers in the US, the UK and in multinationals. We hope that our map can generalize meaningfully to activities in other jurisdictions, but our explanations won’t be accurate for some jurisdictions (e.g. Advocacy & Lobbying is different in China). Whether we use Tran...