一种AI信号，多重人类判断：在线信息传播中基于AI的可信度指标的贝叶斯级联分析
One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread
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中文总结 AI 辅助
该研究将贝叶斯级联模型扩展，以AI为共享信号分析在线信息传播中的人-AI交互，发现过度依赖弱AI有害，多样化AI信号可提升群体信息性。
中文摘要 AI 辅助
社交媒体平台越来越多地使用基于AI的可信度指标来帮助用户判断错误信息。与个体的人-AI决策不同，这些指标嵌入在信息传播过程中：用户既会看到AI预测，也会看到由同一AI形成的早期判断，而他们自己的判断随后可能会进入公共历史。然而，如何从分析上描述这一过程仍未得到充分探索。因此，我们通过将经典贝叶斯级联模型扩展，以AI指标作为共享公共信号，为该场景引入了一种社会学习视角。所得的“网关”条件将AI预测的证据与用户的私人印象进行比较。通过这一视角，我们表明AI改变了公共历史的含义。群体一致性可能反映了累积的独立人类证据，或是对同一AI预测的重复依赖。这造成了一种“保留-修正”权衡：更依赖AI可以保留正确预测，但也会通过阻止修正性的私人印象来锁定错误预测。我们使用关于新闻真实性判断的人类受试者数据来校准该模型。尽管AI的表现优于人类用户，但普通用户对AI的权重低于自身印象，但高于若干同伴的判断，而个体用户的表现从低估AI到足够依赖AI以形成级联不等。模拟显示，过度依赖性能较弱的AI尤其有害，且在用户间多样化AI信号能更好地保持群体的信息性。最后，我们总结了对理解信息传播中人-AI交互以及设计错误信息干预措施的启示。
英文摘要
Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-making, these indicators are embedded in information spread: users see both an AI prediction and earlier judgments shaped by the same AI, and their own judgments may then enter the public history. Yet how to analytically characterize this process remains under-explored. We therefore introduce a social-learning lens for this setting by extending the classical Bayesian cascade model with the AI indicator as a shared public signal. The resulting Gateway condition compares the evidence from the AI prediction with users' private impressions. Through this view, we show that AI changes what public history means. Crowd agreement may reflect accumulated independent human evidence, or repeated dependence on the same AI prediction. This creates a preservation-correction trade-off: stronger reliance on AI can preserve correct predictions, but can also lock in incorrect ones by blocking corrective private impressions. We calibrate the model using human-subject data on news veracity judgments. Although the AI outperforms human users, the average user weights it below her own impression but above several peer judgments, while individual users vary from discounting the AI to relying on it enough to cascade. Simulations show that over-reliance on a weak AI is especially harmful, and that diversifying AI signals across users can better keep the crowd informative. We conclude with implications for understanding human-AI interaction in information spread and designing misinformation interventions.
发表机构
- Purdue University（普渡大学）
- St John’s University（圣约翰大学）
- Stevens Institute of Technology（史蒂文斯理工学院）
- Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工研究与技术联盟）
- University of Manchester（曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
