# One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread

arXiv: https://arxiv.org/abs/2608.30311 · HTML: https://mlsanity.com/paper/2608.30311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 61 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [A Decision Theoretic Framework for Measuring AI Reliance](https://mlsanity.com/paper/2401.15356/) (2024)
- [Hoaxy: A Platform for Tracking Online Misinformation](https://mlsanity.com/paper/1603.01511/) (2016)
- [Community-Based Fact-Checking on Twitter's Birdwatch Platform](https://mlsanity.com/paper/2104.07175/) (2021)
- [Did the Roll-Out of Community Notes Reduce Engagement With Misinformation on X/Twitter?](https://mlsanity.com/paper/2307.07960/) (2023)
- [Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions](https://mlsanity.com/paper/2303.12118/) (2023)
- [Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert Criteria](https://mlsanity.com/paper/2008.09533/) (2020)
- [Do explanations increase the effectiveness of AI-crowd generated fake news warnings?](https://mlsanity.com/paper/2112.03450/) (2021)
- [Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation](https://mlsanity.com/paper/2203.00710/) (2022)
- [Can Community Notes Replace Professional Fact-Checkers?](https://mlsanity.com/paper/2502.14132/) (2025)
- [Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response](https://mlsanity.com/paper/2207.08253/) (2022)
- [Mechanisms of True and False Rumor Sharing in Social Media: Collective Intelligence or Herd Behavior?](https://mlsanity.com/paper/2207.03020/) (2022)
- [All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection](https://mlsanity.com/paper/2601.04160/) (2026)
- [A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations](https://mlsanity.com/paper/2008.00058/) (2020)
- [Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games](https://mlsanity.com/paper/2009.14521/) (2020)
- [When do data visualizations persuade? The impact of prior attitudes on learning about correlations from scatterplot visualizations](https://mlsanity.com/paper/2302.03776/) (2023)
- [AutoRedTrader: Autonomous Red Teaming of Trading Agents through Synthetic Misinformation Injection](https://mlsanity.com/paper/2605.09185/) (2026)
- [Viblio: Introducing Credibility Signals and Citations to Video-Sharing Platforms](https://mlsanity.com/paper/2402.17218/) (2024)
- [Belief Updating and Delegation in Multi-Task Human-AI Interaction: Evidence from Controlled Simulations](https://mlsanity.com/paper/2602.01986/) (2026)
- [Rating Effects on Social News Posts and Comments](https://mlsanity.com/paper/1606.06140/) (2016)
- [When social influence promotes the wisdom of crowds](https://mlsanity.com/paper/2006.12471/) (2020)

## Abstract (arXiv)

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.