Mechanistic interpretability — is the field that dreams of → Enumerative safety

The paper opens by naming a field and closes by naming that field’s most ambitious unmet goal, and deception is the thread connecting them. The introduction frames mechanistic interpretability as a response to a specific worry — that AI systems might "deceive humans in order to accomplish undesirable goals" (Ngo et al., 2022) — and defines the field’s method as understanding how networks calculate their outputs well enough to "reverse engineer parts of their internal processes and make targeted changes to them" (sparse-autoencoders, §"1 Introduction", p. 1). The conclusion returns to the same worry by name: enumerative safety is described as the field’s "ambitious dream" of a complete, human-understandable list of a model’s features sufficient to guarantee it will not perform "dangerous behaviours such as deception" (sparse-autoencoders, §"6.3 Conclusion", p. 9). Read together, the two passages show that enumerative safety is not a separate goal bolted onto the introduction’s framing — it is what the field’s stated concern, reverse-engineering a network well enough to rule out deception, looks like when pushed to its logical completion: not partial reverse-engineering of chosen circuits, but an exhaustive one.