Mechanistic interpretability (often shortened to mechinterp) reframes neural networks not as inscrutable black boxes, but as computational artifacts to be reverse-engineered.
Neural networks, from this viewpoint, resemble complex computational circuits: composed of distinct modules and subroutines whose operations can be teased apart and explicitly understood. By systematically probing models, researchers in mechanistic interpretability seek to identify precisely which algorithms a neural network has learned, how these algorithms are encoded in its parameters, and exactly how computation flows from inputs to outputs.
In April 2025, Dario Amodei published an essay titled "The Urgency of Interpretability," making a passionate case for interpretability research. Amodei set an ambitious goal for Anthropic: by 2027, develop interpretability tools that can reliably detect most problems in AI models. I highly recommend giving it a read.
The best resource for some of the recent work in the field is Anthropic's circuits thread. They regularly publish research, accept research notes by other researchers, and frequently provide updates on their future research directions.
See this wiki (written by Neel Nanda) for an introduction to a lot of the technical terms used in mechinterp. Although it has not been updated since December 2022, it's well written and still very useful.
Mechanistic interpretability thrives within an open, collaborative community, with active discord servers and lots of independent researchers.