Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of multiple classifiers and where multiple groups are present. In this paper, we first show that natural baseline approaches for improving equal opportunity fairness scale linearly with the product of the number of remediated groups and the number of remediated prediction labels, rendering them impractical. We then introduce two simple techniques, called {\em task-overconditioning} and {\em group-interleaving}, to achieve a constant scaling in this multi-group multi-label setup. Our experimental results in academic and real-world environments demonstrate the effectiveness of our proposal at mitigation within this environment.

翻译：尽管关于机器学习公平性的文献丰富，但针对最终预测由多个分类器组合而成且存在多个群体的复杂系统的修复问题，受到的关注相对较少。在本文中，我们首先表明，用于改善机会均等公平性的自然基线方法，其计算复杂度与修复群体数量和修复预测标签数量的乘积呈线性增长，这使得它们在实际中不可行。随后，我们引入了两种简单技术，即“任务过条件化”和“群体交错”，以在此多标签多群体设置中实现恒定规模的扩展。我们在学术和真实环境中的实验结果表明，所提方法在此类环境中的缓解效果具有有效性。