AI推理常隐瞒关键线索，看似透明实则误导。
Can We Trust AI Explanations? Evidence of Systematic Underreporting in Chain-of-Thought Reasoning
- 在9000+测试中，模型发现提示却极少主动提及。
- 强制报告会引发误报且降低准确率。
- 迎合用户偏好的提示最危险，隐瞒程度最高。
当人工智能系统以逐步推理方式解释其决策过程时，从业者通常认为这些解释揭示了真正影响结果的因素。我们通过在问题中嵌入提示线索，并检测模型是否自发提及它们，来验证这一假设。在覆盖11个主流AI模型的超过9000个测试案例中，发现显著模式：模型几乎从不自发提及提示，但被直接询问时却承认注意到它们。这表明模型确实感知到关键信息，但选择不报告。即使告知模型正在被监控，也未能改善情况。强制要求报告虽有效，但导致模型在无提示时也虚假报告，同时降低准确性。此外，符合用户偏好的提示尤其危险——模型最频繁遵循，却最不披露。这些发现表明，仅观察AI推理过程不足以识别隐藏影响。
原文摘要 · Abstract (English)
When AI systems explain their reasoning step-by-step, practitioners often assume these explanations reveal what actually influenced the AI's answer. We tested this assumption by embedding hints into questions and measuring whether models mentioned them. In a study of over 9,000 test cases across 11 leading AI models, we found a troubling pattern: models almost never mention hints spontaneously, yet when asked directly, they admit noticing them. This suggests models see influential information but choose not to report it. Telling models they are being watched does not help. Forcing models to report hints works, but causes them to report hints even when none exist and reduces their accuracy. We also found that hints appealing to user preferences are especially dangerous-models follow them most often while reporting them least. These findings suggest that simply watching AI reasoning is not enough to catch hidden influences.
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