预先的信念和自动的事实检查:基于人工智能的纠正有效性的限制
Kelly M Amaddio1, Jacob T Goebel1, Jason K Clark2
1Department of Psychology, The Ohio State University, Columbus, Ohio, United States of America.
PloS one
|February 5, 2026
概括
当人们持有强烈的先前信念时,自动事实核查器与错误信息作斗争. 人工智能 (AI) 系统的感知精度会影响信念的持久性和信任,强调在干预设计中需要考虑用户的信念.
科学领域:
- 计算社会科学 计算社会科学
- 人与计算机的交互
- 认知心理学 认知心理学
背景情况:
- 错误信息的传播带来了社会挑战,需要有效的识别和缓解策略.
- 自动化事实检查系统提供了潜力,但面临着障碍,特别是在充满信念的环境中.
- 以前的信念可以显著地影响对自动事实检查的反应.
研究的目的:
- 研究先前的信念,对人工智能 (AI) 的信任和自动化事实检查系统的感知准确性之间的相互作用.
- 检查这些因素如何塑造纠正后的错误信息认可和对系统质量的看法.
- 了解对自动事实检查器的反应中的偏见.
主要方法:
- 两项研究 (N=1,372) 涉及参与者接受枪支控制错误信息和自动事实核查器纠正.
- 研究1评估了对系统准确性的默认认知,以及它们与之前的信念和校正后的结果的关系.
- 第二项研究操纵了自动事实检查器 (67%对97%) 的所谓准确性,以观察其对信念持久性和系统感知的影响.
主要成果:
- 以前的信念预测了对错误信息的持续信念,并影响了对事实核查系统的看法.
- 当AI准确度设置为67%时,先前的信念强烈预测了持续的错误信息认可.
- 当准确性信息提供时,更高的感知准确性 (97%) 减弱了先前信念的影响,而先前信念更强烈地与系统可靠性感知有关,特别是在更低的准确性水平.
结论:
- 对自动事实检查器的反应受到个人先前的信念和对系统准确性的看法的影响.
- 设计可扩展的干预措施需要考虑用户的预先存在的信念和他们对事实核查系统特征的评估.
- 了解这些偏见对于制定有效的反错误信息策略至关重要.
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