Mechanistic interpretability

Mechanistic interpretability refers to the detailed understanding of what occurs inside AI models at the individual circuit level. Dario Amodei explains that traditionally, when AI models are trained to be aligned, the internal mechanisms are not clearly understood. Rather than eliminating problematic knowledge and abilities, the model is only taught not to output them. The aim of mechanistic interpretability, therefore, is to grasp the specifics of what happens inside these models to better understand and hopefully solve alignment issues 1.

In another discussion, Dario suggests that mechanistic interpretability may not solve problems directly but will enlighten us about the ongoing processes when attempting to align models. There's a possibility that it could reveal why certain problems persist or are difficult to eradicate, which, in turn, could provide insights into the significant challenges and risks involved in achieving safe AI alignment 2.