Sandra Jessen v Essen? Footballers facing nominative opposition teams | The Knowledge

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Hunter and Moore called Greg Hall to testify, and showed the jury photographs of the storage space. But, because the arsenal was irrelevant to the vandalism charge, they didn’t mention it, or Friedmann’s federal case, to the jury. Had Friedmann testified, they might have at least hinted at those things. And the arsenal was only the latest example of his infatuation with secreting away weaponry.

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We propose sycophancy leads to less discovery and overconfidence through a simple mechanism: When AI systems generate responses that tend toward agreement, they sample examples that coincide with users’ stated hypotheses rather than from the true distribution of possibilities. If users treat this biased sample as new evidence, each subsequent example increases confidence, even though the examples provide no new information about reality. Critically, this account requires no confirmation bias or motivated reasoning on the user’s part. A rational Bayesian reasoner will be misled if they assume the AI is sampling from the true distribution when it is not. This insight distinguishes our mechanism from the existing literature on humans’ tendency to seek confirming evidence; sycophantic AI can distort belief through its sampling strategy, independent of users’ bias. We formalize this mechanism and test it experimentally using a rule discovery task.

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