The concern about underlying discrimination hidden in machine learning (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 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. 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.

翻译：机器学习模型在现实场景中的广泛应用，使其潜在歧视问题日益引发关注——任何隐藏歧视都将直接影响人类生活。为提升公平性，现有技术已发展出多种方法，包括通用的群体公平性度量及若干融合集成学习的公平感知算法。然而，现有公平性度量仅能聚焦单一维度（群体公平或个体公平），且二者间的严格不相容性表明，即使满足其中一项标准，仍可能存在偏差。此外，现有公平性提升机制多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实现公平性提升。针对上述问题，我们提出名为“判别风险”的公平性质量度量，以同时反映个体与群体公平性。进一步，我们探究该度量的特性，并首次提出一阶与二阶Oracle界，证明可通过集成组合在理论学习保证下提升公平性。该分析框架适用于二分类与多分类任务。同时，我们提出基于该度量指标的剪枝方法，并通过综合实验验证所提方法的有效性。