超越二元决策:评估人工智能错误类型对人工智能辅助任务信任和性能的影响
Jin Yong Kim1, Corey Lester1, X Jessie Yang1
1University of Michigan, USA.
Human factors
|March 19, 2025
概括
人工智能错误类型在复杂的决策中显著影响人类的信任和表现. 一些错误误导操作人员,而另一些错误则促使有益的安全检查,强调需要细微的人类-人工智能交互模型.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 认知心理学 认知心理学
背景情况:
- 传统的自动化信任模型通过使用二进制分类过度简化了现实世界的交互.
- 多类分类揭示了影响人类操作者的尚未探索的AI错误模式.
- 现有的研究往往忽视了各种AI错误类型的细微影响.
研究的目的:
- 调查不同的人工智能错误模式如何影响人类操作员的信任和任务性能.
- 为了探索AI错误效应超出二进制决策场景.
- 了解人工智能可靠性对运营商信任和决策的影响.
主要方法:
- 35名参与者在人工智能辅助下进行了模拟的心理旋转任务 (可靠性为70-80%).
- 在60个试验中测量了信任,依赖和表现.
- 混合效应模型分析了五种不同的AI-人类表现模式及其对信任,表现和反应时间的影响.
主要成果:
- 人工智能错误模式显著影响了操作员的性能,反应时间和信任水平.
- 从AI错误的虚假保证导致性能和信任下降.
- 矛盾的是,一些人工智能错误引发了安全检查,尽管信任度有所下降,但提高了性能.
结论:
- 人工智能错误的性质极大地影响了人类的信任和在非二元决策任务中的表现.
- 开发能够考虑多类分类的AI系统对于有效的人类-AI合作至关重要.
- 这些发现强调了人与人工智能交互的复杂性,以及需要先进的建模方法.
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