We study a fair machine learning (ML) setting where an 'upstream' model developer is tasked with producing a fair ML model that will be used by several similar but distinct 'downstream' users. This setting introduces new challenges that are unaddressed by many existing fairness interventions, echoing existing critiques that current methods are not broadly applicable across the diversifying needs of real-world fair ML use cases. To this end, we address the up/down stream setting by adopting a distributional-based view of fair classification. Specifically, we introduce a new fairness definition, distributional parity, that measures disparities in the distribution of outcomes across protected groups, and present a post-processing method to minimize this measure using techniques from optimal transport. We show that our method is able that creates fairer outcomes for all downstream users, across a variety of fairness definitions, and works at inference time on unlabeled data. We verify this claim experimentally, through comparison to several similar methods and across four benchmark tasks. Ultimately we argue that fairer classification outcomes can be produced through the development of setting-specific interventions.
翻译:我们研究一个公平的机器学习(ML)设置,在这个设置中,“上游”模型开发商的任务是制作一个公平的ML模型,供几个类似但不同的“下游”用户使用。这一设置提出了新的挑战,而许多现有的公平干预措施没有解决这些挑战,反映了现有的批评意见,即目前的方法不能广泛适用于现实世界公平 ML使用案例的多样化需要。为此,我们通过对公平分类采用分布式的分布式观点来解决上下流设置问题。具体地说,我们引入一个新的公平定义,即分配均等,衡量受保护群体之间成果分配的差异,并提出后处理方法,利用最佳运输技术将这一措施降到最低。我们表明,我们的方法能够为所有下游用户创造更公平的结果,跨越各种公平定义,并在推论时间就无标签数据开展工作。我们通过比较若干类似的方法和四项基准任务来实验性地核实这一主张。我们最后认为,通过制定具体的干预措施可以产生更公平的分类结果。