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。

构成分类的多组公平性提高的可伸缩解决方案

Towards A Scalable Solution for Improving Multi-Group Fairness in  Compositional Classification

Group bias in natural language processing tasks manifests as disparities in
system error rates across texts authorized by different demographic groups,
typically disadvantaging minority groups. Dataset balancing has been shown to
be effective at mitigating bias, however existing approaches do not directly
account for correlations between author demographics and linguistic variables,
limiting their effectiveness. To achieve Equal Opportunity fairness, such as
equal job opportunity without regard to demographics, this paper introduces a
simple, but highly effective, objective for countering bias using balanced
training. We extend the method in the form of a gated model, which incorporates
protected attributes as input, and show that it is effective at reducing bias
in predictions through demographic input perturbation, outperforming all other
bias mitigation techniques when combined with balanced training.

该研究旨在解决自然语言处理中与作者人口统计信息相关的系统偏差问题，提出了一种基于数据集平衡和带保护属性的门控模型来实现公正性，证明了该方法在减少偏差方面优于其他技术。