How does interpretability help AI safety?

Lens adapted to this topic: Limits, trade-offs, and skeptical arguments

Skeptical perspectives question whether interpretability reliably delivers the kind of understanding safety requires. They emphasize ambiguous definitions, incomplete or misleading explanations, difficulty scaling from toy mechanisms to complex systems, and the possibility that interpretability accelerates capabilities or creates dual-use risks. On this view, interpretability may help in specific cases without being a general solution to advanced AI safety.

• Critics argue that interpretability lacks sufficiently precise definitions, making it difficult to determine whether proposed explanations solve the problems motivating the field.
• Mechanistic interpretability has been criticized for failing so far to provide adequate insight into real-world AI behavior and for relying on a potentially flawed foundational assumption.
• Skeptical analyses argue that many proposed theories of impact are weakly supported and that interpretability may not deserve priority among safety approaches.
• Some critiques argue that mechanistic methods currently set aside human interaction and the broader environment, despite their importance to long-term safety.
• Interpretability research may make powerful systems more capable and introduce dual-use or other risks, creating a safety trade-off rather than a simple benefit.

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