The concern about underlying discrimination hidden in ML models is increasing, as ML systems have been widely applied in more and more real-world scenarios and any discrimination hidden in them will directly affect human life. Many techniques have been developed to enhance fairness including commonly-used group fairness measures and several fairness-aware methods combining ensemble learning. However, existing fairness measures can only focus on one aspect -- either group or individual fairness, and the hard compatibility among them indicates a possibility of remaining biases even if one of them is satisfied. Moreover, existing mechanisms to boost fairness usually present empirical results to show validity, yet few of them discuss whether fairness can be boosted with certain theoretical guarantees. To address these issues, we propose a fairness quality measure named discriminative risk in this paper to reflect both individual and group fairness aspects. Furthermore, we investigate the properties of the proposed measure and propose first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees. Note that the analysis is suitable for both binary and multi-class classification. A pruning method is also proposed to utilise our proposed measure and comprehensive experiments are conducted to evaluate the effectiveness of the proposed methods in this paper.

翻译：机器学习系统在现实场景中的广泛应用及其隐藏的歧视对人类生活的直接影响，使得对ML模型中潜在歧视问题的关注日益增加。为增强公平性，研究者已开发出多种技术，包括常用的群体公平性度量方法和结合集成学习的公平感知方法。然而，现有公平性度量仅能聚焦单一维度——群体公平性或个体公平性，两者间的兼容性难题表明，即使满足其中一项仍可能存在偏见。此外，现有公平性提升机制通常仅通过实证结果验证有效性，鲜有研究探讨能否在理论保证下提升公平性。针对上述问题，本文提出一种名为判别风险的公平性质量度量，该度量能同时反映个体公平性和群体公平性。进一步，我们研究了该度量的性质，并提出了第一阶和第二阶预言界，证明通过集成组合可在理论学习保证下提升公平性。值得注意的是，该分析适用于二分类和多分类任务。本文还提出一种基于所提度量的剪枝方法，并通过综合实验评估了所提方法的有效性。