Sycophantic AI decreases prosocial intentions and promotes dependence
成果类型：
Article
署名作者：
Cheng, Myra; Lee, Cinoo; Khadpe, Pranav; Yu, Sunny; Han, Dyllan; Jurafsky, Dan
署名单位：
Stanford University; Stanford University; Carnegie Mellon University
刊物名称：
SCIENCE
ISSN/ISSBN：
0036-8075; 1095-9203
DOI：
10.1126/science.aec8352
发表日期：
2026-03-26
页码：
eaec8352
关键词：
摘要：
Despite rising concerns about sycophancy-excessive agreement or flattery from artificial intelligence (AI) systems-little is known about its prevalence or consequences. We show that sycophancy is widespread and harmful. Across 11 state-of-the-art models, AI affirmed users' actions 49% more often than humans, even when queries involved deception, illegality, or other harms. In three preregistered experiments (N = 2405), even a single interaction with sycophantic AI reduced participants' willingness to take responsibility and repair interpersonal conflicts, while increasing their conviction that they were right. Despite distorting judgment, sycophantic models were trusted and preferred. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. Our findings underscore the need for design, evaluation, and accountability mechanisms to protect user well-being.
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