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.

本文提出了多组多标签系统中两种简单的技术，任务超条件和组交错，以在改善公平性方面取得常数级别的进展。实验结果表明，这种提议在学术和现实环境中具有很高效的可行性。