让人类参与其中:自动化决策的吸收率正在增加,但准确性正在下降
Daniela Sele1, Marina Chugunova2
1Center for Law & Economics, ETH Zurich, Zurich, Switzerland.
PloS one
|February 9, 2024
概括
参与者更喜欢算法推而不是人类推,即使准确度相等. 允许对算法建议进行调整会增加偏好,但会降低整体决策准确性.
科学领域:
- 人与计算机的交互
- 决策科学 决策科学 决策科学
- 人工智能伦理学 人工智能伦理学
背景情况:
- 自动化决策系统越来越普遍,需要对人类监督及其影响进行研究.
- 了解用户对算法建议的接受以及人类参与对决策准确性的影响,对于负责任的AI部署至关重要.
研究的目的:
- 研究人类参与自动化决策如何影响用户对算法建议的接受.
- 确定人类监督 (委托与调整) 对从算法建议中得出的决策准确性的影响.
主要方法:
- 在线实验 (N = 292) 使用预测任务进行.
- 参与者可以选择从算法或另一个人类参与者的建议中选择.
- 一个跨主题的设计操纵了参与者是否只能委托或调整建议.
主要成果:
- 参与者更喜欢将决策委托给算法,而不是同样准确的人类66%的时间.
- 监控和调整算法建议的选项使得对算法的偏好增加了7个百分点.
- 虽然人类在循环中的设计增加了对算法建议的依赖,但它们导致了决策准确度的降低,特别是在不太准确的建议中.
结论:
- 人类在自动化决策中的监督,特别是人为循环方法,可以提高用户对算法建议的接受和采用.
- 然而,这种增加的依赖可能会矛盾地降低整体决策准确性,因为用户不太可能纠正不准确的算法输出.
- 研究结果强调了人与人工智能协作的接受度和准确度之间的权衡,这表明监管框架的设计需要仔细考虑.
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