可解释性并不能减轻人工智能在人员选择任务中的错误建议的负面影响
Julia Cecil1, Eva Lermer2,3, Matthias F C Hudecek4
1Department of Psychology, LMU Center for Leadership and People Management, LMU Munich, Munich, Germany. julia.cecil@psy.lmu.de.
Scientific reports
|April 28, 2024
概括
人们在人员选择中过度依赖错误的人工智能 (AI) 建议,即使提供了AI解释. 这项研究强调了人工智能监管和人力资源管理质量标准的必要性.
科学领域:
- 人与计算机的交互
- 人力资源中的人工智能
- 组织心理学 组织心理学
背景情况:
- 人工智能 (AI) 决策支持系统越来越多地用于人员选择.
- 这些人工智能系统对人类决策过程的影响仍然不清楚.
- 了解人工智能建议的人际交互对于有效实施至关重要.
研究的目的:
- 研究个人如何在人员选择任务中与人工智能产生的建议互动.
- 检查建议准确性,来源 (人与人工智能) 和可解释性对决策的影响.
- 评估过度依赖不正确的人工智能建议以及可解释性对减轻这种偏见的影响.
主要方法:
- 五个预先注册的实验涉及1403名学生和人力资源管理 (HRM) 员工.
- 操纵建议的准确性 (正确与不正确).
- 操纵建议来源 (人与人工智能) 和人工智能可解释性 (热图与图表).
主要成果:
- 由于过度依赖,不正确的AI建议显著损害了决策性能.
- 参与者未能拒绝不准确的建议,这表明缺乏批判性评估.
- 建议的来源及其可解释性对决策结果的影响有限.
- 增加可解释性并没有减少过度依赖不准确的AI预测.
结论:
- 过度依赖错误的AI建议是人员选择的一个重大挑战.
- 目前的可解释性方法可能不足以克服人工智能辅助决策中的人类偏见.
- 对人工智能在人力资源管理中的监管框架和质量标准至关重要,以确保负责任的使用和降低风险.
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