Neel Nanda: interpretability is useful but nowhere near reliable enough to ensure AI safety
aryaman2020 · x · 2026-10-16
DeepMind interpretability researcher Neel Nanda reiterates that interpretability research might be a big deal and is good enough to be useful, but is nowhere near the quality and reliability needed for anyone to rely on it to ensure AI systems go well.
Quoting him, aryaman adds that as interpretability becomes more central to evaluating alignment, incentives to believe it is "solved" will grow for both good and bad actors — so researchers must relentlessly and skeptically evaluate their own methods.
Related event: Researchers Warn Interpretability Is Far From Solved(2 posts)→
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