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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: Smoke without fire is scary, published by Adam Jermyn on October 4, 2022 on The AI Alignment Forum. This post was written in response to Evan Hubinger’s shortform prompt below and benefited from discussions with him. Thanks to Justis Mills for feedback on this post. If we see precursors to deception (e.g. non-myopia, self-awareness, etc.) but suspiciously don’t see deception itself, that’s evidence for deception. Setup Suppose an AI lab tries to look for the capabilities that enable deceptive alignment. This definitely seems like a good thing for them to do. Say they search for evidence of models optimizing past the current training epoch, for manipulation during human feedback sessions, for situational awareness, and so on. Suppose that when all is said and done they see the following: There are at least two ways to interpret this: “Phew, bigger models are less likely to be deceptively aligned than small ones. Scale away!” “Bigger models are more capable at hiding their deceptive alignment. Run away!” These are very different interpretations, with very different recommendations, so we’d really like to know which to put more credence in. Priors Going in, I think we should have a prior that capabilities getting worse with increasing scale is unlikely. We know of specific examples thanks to the inverse scaling prize, but these are rare exceptions to the general rule of increasing capabilities with increasing scale. As a result, a decreasing deceptive capabilities score should be suspicious. The flip side is that it’s conceivable that some alignment problems get easier with scale. Larger language models understand more of what we say and value (e.g. Figure 8 of Bai+22), and so in some sense outer-aligning them may be easier than outer-aligning smaller models. It’s not inconceivable that the same happens for inner alignment, though I wouldn’t want to rely on this happening. Tests We don’t have to rely on priors alone. Here are a few ways we might distinguish between interpretations: Use transparency tools and figure out what the model is doing. Impair bigger models (e.g. with dropout or random or adversarial perturbations) and see if they get better at some deceptive capabilities. Impairing models shouldn’t improve capabilities, so this would be good evidence that the bigger models are capable of deception but choosing to hide that capability.This experiment is a little tricky to implement, but the core idea is to hope that interventions impair something critical in the model's ability to execute on deception but don't impair all of the deceptive capabilities.Even if the largest models can continue hiding their capabilities despite the impairment, models that are just barely able to hide their capabilities may fail to do so once impaired, which should increase the model size where deceptive capabilities peak. Study the models on a very different data distribution. Models that are hiding relevant capabilities may believe they’ve been deployed and reveal their real goals.Once more, while the largest models may not be fooled by this, models just on the cusp of deceptive alignment are more likely to be tricked and so deceptive alignment may manifest as a shift in the peak of deceptive capabilities towards larger models. Study which specific capabilities get worse with scale. It’s hard to understand how some capabilities could get worse with scale (e.g. situational awareness, planning ability, etc.) so if those are getting worse that’s really worrying, whereas it’s less obvious if e.g. models should/shouldn’t get more epoch-level myopic with scale. Scaling Helps Having access to models of many different sizes is helpful here: we can experiment with small models that are bad at deceptive capabilities, use those results as a baseline, and then try to understand what happens in ...