A new approach to verifying AI outputs suggests that the most effective check is one that the system being tested could not have authored. This method focuses on the provenance of evidence, arguing that true verification lies in where the evidence originates and whether the actor could have manipulated it. The author proposes that the critical boundary for trust is not in immutable storage but at the point of evidence emission, preventing actors from selectively curating data before it is logged. AI
IMPACT Challenges current AI verification paradigms, suggesting a shift towards external, non-authorable checks for robust trust.
RANK_REASON The article presents an opinion on AI verification methods, not a new release or empirical research.
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