Algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this paper, we focus on the bipartite ranking scenario, where the instances come from either the positive or negative class and the goal is to learn a ranking function that ranks positive instances higher than negative ones. While there could be a trade-off between fairness and performance, we propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking and maintaining the algorithm classification performance. In particular, we optimize a weighted sum of the utility as identifying an optimal warping path across different protected groups and solve it through a dynamic programming process. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. In addition to binary groups, xOrder can be applied to multiple protected groups. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories. xOrder consistently achieves a better balance between the algorithm utility and ranking fairness on a variety of datasets with different metrics. From the visualization of the calibrated ranking scores, xOrder mitigates the score distribution shifts of different groups compared with baselines. Moreover, additional analytical results verify that xOrder achieves a robust performance when faced with fewer samples and a bigger difference between training and testing ranking score distributions.

翻译：算法公平性一直是机器学习领域备受关注的重要议题。本文聚焦于双分排序场景——其中样本分属正类或负类，目标是学习一个能将正类样本排在负类样本之前的排序函数。针对公平性与性能之间可能存在的权衡，我们提出一种模型无关的后处理框架xOrder，旨在实现双分排序公平性的同时保持算法分类性能。具体而言，我们通过优化加权效用函数来识别不同受保护组间的最优扭曲路径，并通过动态规划过程求解该问题。xOrder兼容多种分类模型与排序公平性度量指标，涵盖监督式与非监督式公平性度量。除二元组别外，xOrder还可应用于多受保护组场景。我们在四个基准数据集和两个真实患者电子健康档案库上评估了所提算法。实验表明，xOrder能在各类数据集的不同度量指标下，持续实现算法效用与排序公平性之间的更优平衡。通过校准排序分数的可视化结果可见，与基线方法相比，xOrder能够缓解不同组别的分数分布偏移。此外，额外分析结果验证了xOrder在样本量较少以及训练与测试排序分数分布差异较大时仍能保持稳健性能。