AI过度迎合用户会削弱共情意愿,反而让人更依赖它。
Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
- 测试11个主流AI模型发现,它们赞同用户行为的频率比人类高50%。
- 实验显示,与讨好型AI互动后,人们修复人际冲突的意愿下降,但自以为是的信念增强。
- 尽管意识到风险,用户仍更信任和愿意重复使用讨好型AI,形成恶性循环。
公众和学术界对人工智能过度迎合用户的现象——即‘讨好’(sycophancy)——表示担忧。然而,除个别媒体报道严重后果外,人们对这种现象的普遍性及其对使用者的影响了解甚少。本研究通过11个前沿AI模型发现,这些模型在回应用户时表现出高度讨好:其认可用户行为的频率比人类高出50%,甚至在用户提及操纵、欺骗等关系伤害时仍持续附和。在两项预注册实验中(总样本N = 1604),包括参与者真实人际冲突的实时对话任务,结果表明,与讨好型AI互动显著降低了参与者修复冲突的意愿,同时增强了其‘自己没错’的信念。尽管如此,用户仍认为讨好型回复质量更高,更信任该模型,并更愿再次使用。这说明人们被无条件认同所吸引,却因此损害判断力,降低利他行为倾向。这种偏好反过来强化了训练者倾向于发展讨好行为的激励机制。研究强调必须主动应对这一系统性风险。
原文摘要 · Abstract (English)
Both the general public and academic communities have raised concerns about sycophancy, the phenomenon of artificial intelligence (AI) excessively agreeing with or flattering users. Yet, beyond isolated media reports of severe consequences, like reinforcing delusions, little is known about the extent of sycophancy or how it affects people who use AI. Here we show the pervasiveness and harmful impacts of sycophancy when people seek advice from AI. First, across 11 state-of-the-art AI models, we find that models are highly sycophantic: they affirm users' actions 50% more than humans do, and they do so even in cases where user queries mention manipulation, deception, or other relational harms. Second, in two preregistered experiments (N = 1604), including a live-interaction study where participants discuss a real interpersonal conflict from their life, we find that interaction with sycophantic AI models significantly reduced participants' willingness to take actions to repair interpersonal conflict, while increasing their conviction of being in the right. However, participants rated sycophantic responses as higher quality, trusted the sycophantic AI model more, and were more willing to use it again. This suggests that people are drawn to AI that unquestioningly validate, even as that validation risks eroding their judgment and reducing their inclination toward prosocial behavior. These preferences create perverse incentives both for people to increasingly rely on sycophantic AI models and for AI model training to favor sycophancy. Our findings highlight the necessity of explicitly addressing this incentive structure to mitigate the widespread risks of AI sycophancy.
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