人们在算法中看到更多的偏见
Begum Celiktutan1, Romain Cadario1, Carey K Morewedge2
1Rotterdam School of Management, Department of Marketing Management, Erasmus University, Rotterdam 3062 PA, The Netherlands.
概括
人们在算法决策中的偏见比他们自己的更大,即使数据是相同的. 算法中的这种偏见感知可以被利用来纠正人类决策缺陷.
科学领域:
- 认知心理学 认知心理学
- 人与计算机的交互
- 人工智能伦理学 人工智能伦理学
背景情况:
- 算法偏差源于训练数据中存在的人类偏差.
- 了解人工智能偏见的感知对于负责任的开发至关重要.
- 以前的研究还没有完全探索算法中偏差的自我感知.
研究的目的:
- 为了调查个人是否觉得算法决策中的偏见比他们自己的更大.
- 探索潜在的心理机制对算法偏见的感知.
- 确定算法中感知偏差是否可以用于促进 debiasing.
主要方法:
- 涉及研究参与者和算法模拟的实验研究.
- 参与者评估了他们自己的决策中的偏见,在他们的数据上训练的算法决策,以及在别人的数据上训练的算法决策.
- 这些措施包括偏见感知,对偏见盲点的易感性,以及 debiasing 的意图.
主要成果:
- 参与者认为训练在自己的决定上的算法比他们自己的决定有更多的偏见.
- 这种效应甚至在决定相同并提供激励措施时也存在.
- 对偏见盲点的敏感性与对算法比自我更有偏见的感知有关.
- 参与者更倾向于在算法决策上应用 debiasing 校正,而不是他们自己的.
结论:
- 偏见在算法输出中比在一个人的判断中更容易被感知.
- 诸如动机推理之类的认知过程有助于这种感知.
- 算法可以作为揭示和纠正人类偏见的工具.
- 这对设计促进更公平决策的AI系统有影响.
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