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 checks generated by a popular AI model on belief in, and sharing intent of, political news in a preregistered randomized control experiment. Although the AI 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. However, the AI fact-checker is harmful in specific cases: it decreases beliefs in true headlines that it mislabels as false and increases beliefs for false headlines that it is unsure about. On the positive side, the AI increases sharing intents for correctly labeled true headlines. When participants are given the option to view AI 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：事实核查可作为应对虚假信息的有效策略，但网上信息量庞大使其难以大规模实施。近年的人工智能（AI）语言模型在事实核查任务中展现出令人印象深刻的能力，但目前尚不明确人类如何与AI模型生成的事实核查信息进行互动。本研究通过预先注册的随机对照实验，考察流行AI模型生成的事实核查结果对政治新闻可信度及分享意愿的影响。尽管AI在揭穿虚假标题方面表现尚可，但研究发现它并未显著提升参与者辨别标题准确性或分享真实新闻的能力。然而，AI事实核查器在特定情况下存在危害：它会降低参与者对误判为假新闻的真实标题的信任度，同时增强参与者对AI无法确定真伪的虚假标题的信任。积极方面在于，AI能提升参与者对正确标注的真实标题的分享意愿。当参与者可自主选择查看AI事实核查结果并主动执行时，他们分享真假新闻的意愿显著增强，但仅对假新闻的信任度有所提升。本研究揭示了AI应用潜在危害的重要来源，并强调制定政策以防止或缓解此类非预期后果的迫切需求。