Interpretability Will Not Reliably Find Deceptive AI — LessWrong

Disclaimer: Post written in a personal capacity. These are personal hot takes and do not in any way represent my employer's views TL;DR: There’s a common, often implicit, argument made in AI safety discussions: interpretability is presented as the only reliable path forward for detecting deception in advanced AI - e.g. as argued for in Dario Amodei’s recent “The Urgency of Interpretability”.[1] I disagree with this claim. The conceptual reasoning is simple and compelling: a sufficiently sophisticated deceptive AI can say whatever we want to hear, perfectly mimicking aligned behavior externally. But faking its internal cognitive processes – its "thoughts" – seems much harder. Therefore, goes the argument, we must rely on interpretability to truly know if an AI is aligned. I am concerned this line of reasoning represents an isolated demand for rigor. It correctly identifies the deep flaws in relying solely on external behavior (black-box methods) but implicitly assumes that interpretabil

x Interpretability Will Not Reliably Find Deceptive AI — LessWrong Interpretability (ML & AI) AI Curated 2025 Top Fifty: 38 % 342 Interpretability Will Not Reliably Find Deceptive AI by Neel Nanda 4th May 2025 AI Alignment Forum 9 min read 69 342 Ω 117 Disclaimer: Post written in a personal capacity. These are personal opinions and do not in any way represent my employer's views TL;DR: I do not think we will produce high reliability methods to evaluate or monitor the safety of superintelligent systems via current research paradigms, with interpretability or otherwise. Interpretability still se

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