与可犯错的人工智能互动:当收到人工智能错误分类时,不信任是否有帮助?
Tobias M Peters1, Ingrid Scharlau1
1Department of Psychology, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany.
Frontiers in psychology
|June 11, 2025
概括
鼓励对人工智能 (AI) 建议持怀疑态度,并不会改善性能,可能会使其恶化. 对人工智能的信任增加,而不信任减少,即使人工智能的准确性下降,突出了复杂的人类-人工智能交互动态.
科学领域:
- 人与计算机的交互
- 人工智能伦理学 人工智能伦理学
- 认知心理学 认知心理学
背景情况:
- 可信的人工智能 (AI) 在医学等高风险领域至关重要.
- 目前的研究往往侧重于培养用户对人工智能的信任,以提高性能.
- 然而,批判性地审查和不信任人工智能决策的能力也是至关重要的.
研究的目的:
- 调查怀疑指令对人类-人工智能交互性能的影响.
- 分析随着人工智能性能下降,对人工智能的信任和不信任如何发展.
- 用贝叶斯信号检测理论量化适当的依赖.
主要方法:
- 创建了两个图像分类场景,并提供模拟人工智能建议.
- 参与者接受了AI关于质量下降的建议.
- 测量了任务执行,信任和不信任,条件包括怀疑指令与中立指令.
主要成果:
- 怀疑指令没有改善,事实上恶化了参与者的表现.
- 随着人工智能质量下降,人们对人工智能的信任增加了,不信任减少了,无论指示如何.
- 贝叶斯信号检测理论分析提供了一种方法来评估适当的依赖.
结论:
- 指示用户对人工智能持怀疑态度并不会提高性能,可能会有害.
- 用户对人工智能的信任动态是复杂的,尽管准确性下降,但显示了用户信任的增加.
- 这些发现对人工智能免责声明和对人工智能系统的信任和不信任的未来研究有影响.
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