How Can Interpretability Researchers Help AGI Go Well? — AI Alignment Forum

Reasoning Model Interpretability: basic science of how to interpret computation involving the chain of thought [4] Automating Interpretability: eg interpretability agents Finding Good Proxy Tasks: eg building good model organisms Model Diffing Discovering Unusual Behaviours Data-Centric Interpretability: building better methods to extract insights from large datasets Applied Interpretability To apply our pragmatic philosophy, we need North Stars to aim for. We do this work because we're highly concerned about existential risks from AGI and want to help ensure AGI goes well. One way we find our North Stars is by thinking through theories of change where interpretability researchers can help AGI go well (which goes well beyond “classic” mech interp) Our thoughts below are highly informed by our takes on the comparative advantages of mech interp researchers, as discussed in the accompanying post: Notably, we think that while interpretability excels at producing qualitative insights (inclu

x How Can Interpretability Researchers Help AGI Go Well? — AI Alignment Forum GDM Interp Progress Updates Interpretability (ML & AI) AI Frontpage 33 How Can Interpretability Researchers Help AGI Go Well? by Neel Nanda , Josh Engels , Senthooran Rajamanoharan , Arthur Conmy , bilalchughtai , CallumMcDougall , János Kramár , lewis smith 1st Dec 2025 17 min read 1 33 Executive Summary Over the past year, the Google DeepMind mechanistic interpretability team has pivoted to a pragmatic approach to interpretability, as detailed in our accompanying post [1] , and are excited for more in the field to

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