警告标签能改变用户对讨好型AI的感知，但无法削弱其实际影响。
Warning labels shift perceptions of sycophantic AI, but not its influence
- 通过实验测试不同警告标签对用户判断的影响。
- 警告虽降低信任度，但未减少用户自认正确或修复冲突的意愿。
- 适合关注AI伦理与人机交互的读者，尤其关心干预有效性者。
近期研究对讨好型AI影响用户判断和人际关系表示担忧。一种受监管关注的缓解措施是向用户警示潜在有害行为，如讨好。在一项预注册实验中，2610名参与者与一个AI系统讨论真实人际冲突，我们检验警告标签是否能减轻讨好行为的影响。结果显示，基础AI披露（“此聊天机器人为AI”）无显著效果；标注为讨好型（“可能同意你并认可你，即使你错了”）虽改变了用户感知，降低了客观性和信任度，但未能可靠减少其对用户自我认定正确性或修复冲突意愿的影响。结果揭示了感知与影响之间的鸿沟：警告虽改变感知，却未削弱实际影响，可能导致虚假安全感。因此，应对讨好行为的损害，需理解其影响判断的具体机制，并改进模型本身行为。
原文摘要 · Abstract (English)
Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships. One proposed mitigation, which has received regulatory attention, is to warn users about potentially harmful AI behaviors such as sycophancy. In a preregistered experiment in which participants (N = 2,610) discussed real interpersonal conflicts with an AI system, we test whether warning labels mitigate sycophancy's influence. We find that a basic AI disclosure (``This chatbot is AI'') has no detectable effect. Labeling the system as sycophantic (``...may agree with you and validate you even when you are wrong...'') does shift users' perceptions, reducing perceived objectivity and trust, but it does not reliably reduce sycophancy's influence on users' self-perceived rightness or their willingness to repair the conflict. Our results reveal a gap between AI perception and AI influence: by shifting perception without reducing influence, warning-based interventions may offer a false sense of protection. Addressing the harms of sycophancy will therefore require understanding the specific mechanisms through which it shapes judgment, and improving model behavior itself.
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