As the bias issue is being taken more and more seriously in widely applied machine learning systems, the decrease in accuracy in most cases deeply disturbs researchers when increasing fairness. To address this problem, we present a novel analysis of the expected fairness quality via weighted vote, suitable for both binary and multi-class classification. The analysis takes the correction of biased predictions by ensemble members into account and provides learning bounds that are amenable to efficient minimisation. We further propose a pruning method based on this analysis and the concepts of domination and Pareto optimality, which is able to increase fairness under a prerequisite of little or even no accuracy decline. The experimental results indicate that the proposed learning bounds are faithful and that the proposed pruning method can indeed increase ensemble fairness without much accuracy degradation.

翻译：随着偏见问题在广泛应用的机器学习系统中日益受到重视，在提升公平性时准确率下降的现象严重困扰着研究人员。针对这一问题，我们提出了一种基于加权投票的预期公平性质量新分析方法，该方法同时适用于二分类和多分类任务。该分析考虑了集成成员对偏见预测的修正作用，并提供了易于高效优化的学习边界。我们进一步基于该分析以及支配与帕累托最优概念，提出了一种剪枝方法，能够在确保准确率几乎不下降甚至无下降的前提下提升公平性。实验结果表明，所提出的学习边界具有可靠性，且该剪枝方法确实能够在准确率损失较小的条件下提升集成模型的公平性。