Open Problems in Mechanistic Interpretability
Lee Sharkey, Bilal Chughtai, Joshua Batson et al.
2025 · ArXiv.org · 5 citations
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to revea…
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