Mechanistic interpretability provides tools to understand AI model internals, but understanding alone doesn't guarantee safety—it must be combined with behavioral evaluation and other safety measures to be effective.
This comprehensive survey examines mechanistic interpretability—the study of how neural networks work internally—and whether understanding a model's inner workings actually makes it safer. It covers foundational concepts like features and circuits, surveys analysis methods, and explores how this understanding can help with safety through techniques like knowledge editing and steering.