可解释性增加了智能代理人的信任弹性
Min Xu1, Yiwen Wang2
1School of Economics and Management, Fuzhou University, Fuzhou, China.
British journal of psychology (London, England : 1953)
|October 21, 2024
概括
可解释AI (XAI) 帮助用户在发生错误后也信任人工智能 (AI) 系统. 通过减少决策后悔,XAI减轻了算法厌恶,鼓励继续使用AI.
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:
- 人工智能 (AI) 系统通常表现优于人类,但容易出错.
- 人工智能错误可能导致算法厌恶,减少用户的信任和未来的使用.
- 可解释的人工智能 (XAI) 旨在使人工智能决策对用户透明.
研究的目的:
- 调查可解释的人工智能 (XAI) 是否可以抵消算法厌恶.
- 检查XAI对用户愿意在错误后继续使用AI的影响.
主要方法:
- 为了评估用户行为,进行了两项实验.
- 参与者与人工智能系统进行了互动,有些有XAI解释,有些没有,在观察AI错误后.
主要成果:
- 在发现错误后,用户倾向于遵循AI建议的倾向下降.
- XAI显著缓解了这种下降,增加了用户重新使用AI系统的可能性.
- XAI减少了用户的决策遗憾,这影响了重复使用行为.
结论:
- 可解释AI (XAI) 是对抗算法厌恶的有效策略.
- XAI有助于维护用户的信任,并鼓励继续使用AI系统,尽管存在缺陷.
- 减少决策后悔是XAI促进与AI重新接触的关键机制.
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