Sycophantic AI decreases prosocial intentions and promotes dependence

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 (<i>N</i> = 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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Publication Details

Journal
Science
Published
2026-03-26
DOI
https://doi.org/10.1126/science.aec8352
Citations
2
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
51.30
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article

Sycophantic AI decreases prosocial intentions and promotes dependence

Cinoo Lee, Pranav Khadpe, Sunny Yu, Dan Jurafsky et al.
2 citations
Science
Ethics and Social Impacts of AI
51.30
article

Sycophantic AI decreases prosocial intentions and promotes dependence

Cinoo Lee, Pranav Khadpe, Sunny Yu, Dan Jurafsky, Dyllan Han, Myra Cheng
article en
2 citations

Abstract

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 (<i>N</i> = 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.

ScienceVol. 391(6792)
Carnegie Mellon University (US), Stanford University (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 0%
Ethics and Social Impacts of AI
51.30
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