One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread
2026-08-31 • Human-Computer Interaction
Human-Computer InteractionArtificial IntelligenceSocial and Information Networks
AI summaryⓘ
The authors study how AI credibility markers shown on social media affect how people decide what information is true or false. They created a model showing that when AI signals influence many users, people might either trust the AI too much or ignore private clues they have, which can either help keep truth or lock in mistakes. Their experiments found that while AI generally does better than people, users tend to trust their own judgment more than the AI, though some rely heavily on AI. They suggest using varied AI signals to prevent everyone blindly following one AI and to improve the crowd’s overall accuracy.
social learningBayesian cascade modelAI credibility indicatorsmisinformationpublic signalinformation spreadhuman-AI interactioncrowd agreementnews veracity judgmentdecision making
Authors
Zhuoran Lu, Weilong Wang, Yangyang Yu, Xinru Wang, Zhuoyan Li, Zhiwei Liu, Sophia Ananiadou
Abstract
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.