Open Problems in Mechanistic Interpretability
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arXiv
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| Format: | Preprint |
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2025
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| author | Sharkey, Lee Chughtai, Bilal Batson, Joshua Lindsey, Jack Wu, Jeff Bushnaq, Lucius Goldowsky-Dill, Nicholas Heimersheim, Stefan Ortega, Alejandro Bloom, Joseph Biderman, Stella Garriga-Alonso, Adria Conmy, Arthur Nanda, Neel Rumbelow, Jessica Wattenberg, Martin Schoots, Nandi Miller, Joseph Michaud, Eric J. Casper, Stephen Tegmark, Max Saunders, William Bau, David Todd, Eric Geiger, Atticus Geva, Mor Hoogland, Jesse Murfet, Daniel McGrath, Tom |
| author_facet | Sharkey, Lee Chughtai, Bilal Batson, Joshua Lindsey, Jack Wu, Jeff Bushnaq, Lucius Goldowsky-Dill, Nicholas Heimersheim, Stefan Ortega, Alejandro Bloom, Joseph Biderman, Stella Garriga-Alonso, Adria Conmy, Arthur Nanda, Neel Rumbelow, Jessica Wattenberg, Martin Schoots, Nandi Miller, Joseph Michaud, Eric J. Casper, Stephen Tegmark, Max Saunders, William Bau, David Todd, Eric Geiger, Atticus Geva, Mor Hoogland, Jesse Murfet, Daniel McGrath, Tom |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open Problems in Mechanistic Interpretability Sharkey, Lee Chughtai, Bilal Batson, Joshua Lindsey, Jack Wu, Jeff Bushnaq, Lucius Goldowsky-Dill, Nicholas Heimersheim, Stefan Ortega, Alejandro Bloom, Joseph Biderman, Stella Garriga-Alonso, Adria Conmy, Arthur Nanda, Neel Rumbelow, Jessica Wattenberg, Martin Schoots, Nandi Miller, Joseph Michaud, Eric J. Casper, Stephen Tegmark, Max Saunders, William Bau, David Todd, Eric Geiger, Atticus Geva, Mor Hoogland, Jesse Murfet, Daniel McGrath, Tom Machine Learning 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. |
| title | Open Problems in Mechanistic Interpretability |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.16496 |