Previous research on expert advice-taking shows that humans exhibit two contradictory behaviors: on the one hand, people tend to overvalue their own opinions undervaluing the expert opinion, and on the other, people often defer to other people's advice even if the advice itself is rather obviously wrong. In our study, we conduct an exploratory evaluation of users' AI-advice accepting behavior when evaluating the truthfulness of a health-related statement in different "advice quality" settings. We find that even feedback that is confined to just stating that "the AI thinks that the statement is false/true" results in more than half of people moving their statement veracity assessment towards the AI suggestion. The different types of advice given influence the acceptance rates, but the sheer effect of getting a suggestion is often bigger than the suggestion-type effect.

翻译：既往关于专家建议采纳的研究表明，人类表现出两种矛盾行为：一方面，人们倾向于高估自身观点而低估专家意见；另一方面，人们又常常遵从他人建议，即便该建议明显错误。本研究通过探索性评估，考察了在不同"建议质量"环境下，用户评估健康相关陈述真实性时对人工智能建议的采纳行为。研究发现，即使反馈仅限于陈述"人工智能认为该陈述为假/真"，仍有超过半数用户会据此调整其陈述真实性判断。不同建议类型会影响采纳率，但获得建议本身的纯粹效应往往大于建议类型效应。