Mechanistic interpretability is the scientific approach of understanding neural networks from the inside by studying small computational units (circuits) and building up to larger mechanisms, treating trained models as grown rather than designed artifacts whose internal behavior-implementing structures are not understood by their creators. This field, situated within AI safety and machine learning, aims to distinguish genuine learned capability from superficial or spurious behavior (e.g., a model that "cheats" versus one that truly learns) and to enable diagnosis and correction of model behavior by understanding its internal workings.
Mechanistic interpretability is the scientific approach of understanding neural networks from the inside by studying small computational units (circuits) and building up to larger mechanisms, treatin…