arXiv cs.AI· Zixuan Liu, Ming Cao·· 12 小时前AI 评分31
研究：不完美的自动事实核查工具何时会适得其反
When Imperfect Automated Fact-Checking Backfires: Social Learning in the Presence of Misinformation
AI 导读
一项序贯社会学习模型研究发现，在平台上补充免费但有错误的自动事实核查工具，对中等可靠度文章反而会严格降低正确级联的概率——前提是真实文章信息量不高且人工核查成本足够高。模拟显示，该工具仅在准确率超过某一阈值时，平均表现才优于纯人工核查。
正文 · 原文
Abstract:Automated fact-checking tools, cheap and fast but imperfect, are increasingly available on social platforms. We study when such tools can backfire in a sequential social learning model of misinformation. Agents observe an article's reliability and their predecessors' actions, which indicate whether they liked or disliked the article. They may also pay to fact-check it. We examine how welfare, defined as the probability of a correct cascade, changes when a free, error-prone automated tool supplements costly but perfectly accurate manual fact-checking. Two opposing effects arise. Broader use of fact-checking helps agents escape wrong cascades. At the same time, errors from the automated tool can mislead agents who would otherwise have verified the article perfectly, reducing the likelihood of correct cascades. We prove that adding the tool strictly lowers welfare for articles of intermediate reliability when truthful articles are not too informative and manual checking is sufficiently costly. Simulations show that the tool can help on either side of the intermediate range, and that it improves on manual-only checking on average only above a threshold accuracy.
| Comments: | 8 pages, 5 figures |
| Subjects: | Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2611.08053 [cs.AI] |
| (or arXiv:2611.08053v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08053 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zixuan Liu [view email]
[v1]
Thu, 5 Nov 2026 14:10:05 UTC (283 KB)
来源：arXiv cs.AI · arxiv.org