在美国,公众对算法决策中的准确性-公平性权衡的看法
Mehdi Mourali1, Dallas Novakowski1, Ruth Pogacar1
1Haskayne School of Business, University of Calgary, Calgary, Alberta, Canada.
PloS one
|March 13, 2025
概括
关于算法公平性的公众态度揭示了平等待遇算法 (ETA) 和平等影响算法 (EIA) 之间的权衡. 随着准确性差距的扩大,对EIA的支持减少了,党派分歧影响了偏好公平而不是准确性的偏好.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 公共政策 公共政策
背景情况:
- 算法决策模型往往排除受保护的属性以防止歧视,这种方法被称为"平等待遇".
- 尽管平等待遇,算法仍然可以产生不同的人口群体不平等的影响.
- 其他公平性概念,如"平等影响算法" (EIA),旨在减少这些差异,但可能会损害预测准确性.
研究的目的:
- 调查美国公众对算法决策中的预测准确性和公平性之间的权衡的态度.
- 了解个人何时优先考虑具有更高准确度的平等待遇算法 (ETA) 与减少绩效差距的平等影响算法 (EIA).
主要方法:
- 使用随机联合实验来评估公众的偏好.
- 进行了后续选择实验,以进一步检查这些态度.
主要成果:
- 随着EIA和ETA之间的准确性差距的增加,对EIA的支持显著下降.
- 当预期时间导致结果存在重大差异时,对影响平价的优先考虑更大.
- 党派身份强烈影响了偏好,民主党人赞成影响平价,共和党人赞成准确度最大化.
- 性别和社会正义方向也预测对EIAs的支持.
结论:
- 公众对算法公平性的态度是由多个因素驱动的,这突出了平等和公平原则之间的社会划分.
- 实现公平的人工智能 (AI) 标准需要通过有效的治理来解决这些相互矛盾的人类价值观.
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