LessWrong (Curated & Popular) “Interpretability Will Not Reliably Find Deceptive AI” by Neel Nanda
May 5, 2025
Neel Nanda, a thought leader on AI safety, shares his intriguing insights on interpretability and its limits. He argues that relying solely on interpretability to detect deceptive AI is naive. Instead, he advocates for a multi-faceted defense strategy that includes black-box methods alongside interpretability. Nanda emphasizes that while interpretability can enhance our understanding, it's just one layer in ensuring AI safety. His hot takes spark a provocative discussion on the challenges we face with superintelligent systems.
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Interpretability Is Necessary but Insufficient
- Interpretability is valuable but not sufficient alone to guarantee detection of deceptive AI.
- It should be part of a layered defense strategy alongside other safeguards for better reliability.
High Reliability Unlikely for Superintelligence Safety
- High reliability from interpretability or black-box methods for superintelligence safety seems unattainable.
- We must pragmatically use multiple imperfect methods to reduce risk rather than expect near certainty.
Core Limitations of Interpretability
- Interpretability faces persistent challenges like missing features and interpretative errors.
- Proving a model is free of deception is incredibly hard, especially at needed confidence levels for deployment.