Their makers claim they can detect dozens of cancer types — but some scientists say they could be missing many cancers or delivering the wrong diagnosis.
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.
，更多细节参见51吃瓜
最糟糕的時候，他曾經擔心過自己的庇護申請會被拒絕、並且會被遣返回中國，「我又瞭解到我的這個法官的（庇護申請）通過率也不是很高……我當時就感覺這個大環境實在是太糟了，我這個案子也可能過不了。」
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