揭示AI推理过程会增加信任，但会挤出独特人类知识
Revealing AI Reasoning Increases Trust but Crowds Out Unique Human Knowledge
AI总结：
本文通过一项激励相容实验发现，展示AI推理过程会显著增加人类信任，但会诱导过度信任并挤出独特人类知识的利用，呼吁改进人机协作中的信息设计。
摘要：
有效的人机协作要求人类准确评估AI能力并相应校准其信任。人类通常拥有依赖于情境的私有信息，称为独特人类知识（UHK），这对于决定是接受还是推翻AI的建议至关重要。我们通过一项预先注册的、激励相容的实验（N = 752）考察了展示AI推理过程如何影响信任和UHK的利用。我们发现，揭示AI推理过程——无论是简短的还是详尽的——都作为一种强有力的说服性启发式，显著增加了对AI建议的信任和认同。这种透明度并没有帮助参与者适当地校准他们的信任，反而诱导了过度信任，从而挤出了UHK的利用。我们的结果强调，在揭示AI推理过程时需要谨慎考虑，并呼吁在人机协作系统中进行更好的信息设计。
英文摘要：
Effective human-AI collaboration requires humans to accurately gauge AI capabilities and calibrate their trust accordingly. Humans often have context-dependent private information, referred to as Unique Human Knowledge (UHK), that is crucial for deciding whether to accept or override AI's recommendations. We examine how displaying AI reasoning affects trust and UHK utilization through a pre-registered, incentive-compatible experiment (N = 752). We find that revealing AI reasoning, whether brief or extensive, acts as a powerful persuasive heuristic that significantly increases trust and agreement with AI recommendations. Rather than helping participants appropriately calibrate their trust, this transparency induces over-trust that crowds out UHK utilization. Our results highlight the need for careful consideration when revealing AI reasoning and call for better information design in human-AI collaboration systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
