Inside the Black Box: A Practical Field Guide to Mechanistic Interpretability

Researchers argue that Mechanistic interpretability is the most effective technical strategy for ensuring AI alignment and safety. While traditional methods like reinforcement learning from human feedback focus on outward behavior, they are vulnerable to "reward hacking" and deception. In contrast, mechanistic interpretability seeks to reverse-engineer neural networks by mapping the internal circuits and features that drive specific computations.