Open Problems in Mechanistic Interpretability
Authors: name: Lee Sharkey, name: Bilal Chughtai, name: Joshua Batson, name: Jack Lindsey, name: Jeff Wu, name: Lucius Bushnaq, name: Nicholas Goldowsky-Dill, name: Stefan Heimersheim, name: Alejandro Ortega, name: Joseph Bloom, name: Stella Biderman, name: Adria Garriga-Alonso, name: Arthur Conmy, name: Neel Nanda, name: Jessica Rumbelow, name: Martin Wattenberg, name: Nandi Schoots, name: Joseph Miller, name: Eric J. Michaud, name: Stephen Casper, name: Max Tegmark, name: William Saunders, name: David Bau, name: Eric Todd, name: Atticus Geiger, name: Mor Geva, name: Jesse Hoogland, name: Daniel Murfet, name: Tom McGrath

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.