Fact checking can be an effective strategy against misinformation, but its implementation at scale is impeded by the overwhelming volume of information online. Recent artificial intelligence (AI) language models have shown impressive ability in fact-checking tasks, but how humans interact with fact-checking information provided by these models is unclear. Here, we investigate the impact of fact-checking information generated by a popular large language model (LLM) on belief in, and sharing intent of, political news in a preregistered randomized control experiment. Although the LLM performs reasonably well in debunking false headlines, we find that it does not significantly affect participants' ability to discern headline accuracy or share accurate news. Subsequent analysis reveals that the AI fact-checker is harmful in specific cases: it decreases beliefs in true headlines that it mislabels as false and increases beliefs in false headlines that it is unsure about. On the positive side, the AI fact-checking information increases sharing intents for correctly labeled true headlines. When participants are given the option to view LLM fact checks and choose to do so, they are significantly more likely to share both true and false news but only more likely to believe false news. Our findings highlight an important source of potential harm stemming from AI applications and underscore the critical need for policies to prevent or mitigate such unintended consequences.

翻译：事实核查可以是应对虚假信息的有效策略，但其大规模实施受限于网络信息的海量性。近年来，人工智能语言模型在事实核查任务中展现出令人瞩目的能力，但人类如何与这些模型提供的事实核查信息互动尚不明确。我们在预先注册的随机对照实验中，研究了由流行的大型语言模型生成的事实核查信息对政治新闻可信度认知及分享意图的影响。尽管该语言模型在驳斥虚假标题方面表现尚可，但我们发现它并未显著提升参与者辨别标题准确性或分享准确新闻的能力。后续分析揭示，人工智能事实核查器在特定情况下具有危害性：它会降低参与者对误判为虚假的真实标题的信任度，同时增加对其不确定的虚假标题的信任度。从积极方面看，人工智能事实核查信息能提升对正确标注的真实标题的分享意图。当参与者有权选择查看大型语言模型事实核查结果并主动选择查看时，他们显著更倾向于同时分享真实与虚假新闻，但仅更倾向于相信虚假新闻。我们的研究结果凸显了人工智能应用潜在危害的重要来源，并强调亟需制定政策来预防或减轻此类意外后果。