双边排名公平性通过一个模型不可知秩序调整
IEEE transactions on pattern analysis and machine intelligence
|October 11, 2023
概括
我们介绍了xOrder,这是一个模型不可知的框架,用于实现双边排名中的公平性. 它平衡了保护群体之间的算法实用性和公平性,而不会影响性能.
科学领域:
- 机器学习 机器学习
- 算法公平性 算法公平性
- 数据科学数据科学数据科学
背景情况:
- 算法公平性是机器学习中日益关注的问题,特别是在排名场景中.
- 学习的排名函数可以在不同的受保护群体中表现出系统的差异.
- 现有的方法可能会在公平性和分类性能之间进行权衡.
研究的目的:
- 为实现双边排名的公平性,提出一个模型无关的后处理框架,xOrder.
- 为了保持算法分类性能,同时确保公平.
- 为解决跨二进制和多重受保护群体的公平性问题.
主要方法:
- xOrder通过在受保护组中识别最佳曲线路径来优化加权的实用总和.
- 优化是使用动态编程过程来解决的.
- 该框架与各种分类模型和公平度指标 (监督和无监督) 兼容.
主要成果:
- 在不同的数据集和指标中,xOrder 始终在算法实用性和排名公平性之间取得了更好的平衡.
- 可视化显示,xOrder减轻了与基线相比,群体之间的分数分配转移.
- 分析结果证实了在有限的样本和训练测试分布差异下强大的性能.
结论:
- 在不牺牲业绩的情况下,xOrder有效地提高了双边排名的公平性.
- 该框架提供了一种灵活而强大的解决方案,以缓解算法差异.
- xOrder证明了对基准和现实世界电子健康记录数据的实际应用.
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