通过先发制人的来源质疑和揭穿AI产生的错误信息来应对.
Emily R Spearing1, Constantina I Gile1, Amy L Fogwill1
1School of Psychological Science, The University of Western Australia, Perth, Western Australia, Australia.
Royal Society open science
|June 26, 2025
概括
预防性人工智能信息信任减少并没有阻止错误信息的影响. 只有揭穿,特别是与注射相结合,才能有效地抵消人工智能对推理产生错误信息的影响.
科学领域:
- 认知心理学 认知心理学
- 人工智能伦理学 人工智能伦理学
- 信息科学 信息科学 信息科学
背景情况:
- 人们普遍担心人工智能产生的错误信息.
- 人工智能错误信息对人类推理的影响以及对策的有效性尚未得到充分理解.
- 对人工智能产生的信息的信任是影响其影响的关键因素.
研究的目的:
- 调查以源为中心的注射策略在减少人工智能产生的错误信息对推理的影响方面的有效性.
- 为了比较疫苗接种与追溯揭露和简单的免责声明.
- 评估对人工智能产生的信息信任的可塑性.
主要方法:
- 进行了两项涉及1223名参与者的实验.
- 参与者接触到一个误导性的AI产生的文章.
- 干预措施包括预防性接种疫苗,追溯性揭露,信任增强和免责声明.
主要成果:
- 一篇误导性的AI文章影响了推理,而不管其声明的来源 (人类或AI).
- 接种疫苗降低了对人工智能信息的一般信任,但并没有降低其对推理的特定影响.
- 揭露有效地减少了错误信息的影响,联合注射和揭露完全消除了它.
结论:
- 生成型人工智能可以成为一种强大的错误信息来源,影响人类的推理.
- 仅靠以源为中心的注射就不足以抵消人工智能错误信息对推理的影响.
- 为了有效减轻人工智能产生的错误信息的影响,需要采取多种对策,特别是揭露.
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