论文标题
与重叠组的公平性
Fairness with Overlapping Groups
论文作者
论文摘要
在算法上公平的预测问题中，一个标准目标是确保同时多个重叠组的公平度指标的平等性。我们使用概率人群分析重新考虑了这个标准的公平分类问题，这反过来揭示了贝叶斯最佳分类器。我们的方法统一了各种现有的团体成绩分类方法，并可以扩展到广泛的不可兼容的多类绩效指标和公平度量。贝叶斯最佳分类器进一步激发了与重叠组在算法上公平分类的一致程序。在各种真正的数据集上，拟议的方法就其公平性 - 性能折衷而优于基准。
In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this standard fair classification problem using a probabilistic population analysis, which, in turn, reveals the Bayes-optimal classifier. Our approach unifies a variety of existing group-fair classification methods and enables extensions to a wide range of non-decomposable multiclass performance metrics and fairness measures. The Bayes-optimal classifier further inspires consistent procedures for algorithmically fair classification with overlapping groups. On a variety of real datasets, the proposed approach outperforms baselines in terms of its fairness-performance tradeoff.
