title: Open Problems in Mechanistic Interpretability
summary: 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 reveal deeper
  insights; we must figure out how best to apply our methods in pursuit of
  specific goals; and the field must grapple with socio-technical challenges that
  influence and are influenced by our work. This forward-facing review discusses
  the current frontier of mechanistic interpretability and the open problems that
  the field may benefit from prioritizing.
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paper_title: Open Problems in Mechanistic Interpretability
paper_summary: 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 reveal deeper
  insights; we must figure out how best to apply our methods in pursuit of
  specific goals; and the field must grapple with socio-technical challenges that
  influence and are influenced by our work. This forward-facing review discusses
  the current frontier of mechanistic interpretability and the open problems that
  the field may benefit from prioritizing.
paper_ai_summary: Mechanistic interpretability aims to understand neural network mechanisms to enhance AI assurance and address scientific questions about intelligence, highlighting open problems in methods, application, and socio-technical challenges.
paper_ai_keywords: mechanistic interpretability neural networks AI system behavior intelligence conceptual improvements practical improvements specific goals socio-technical challenges