Abstract
When building classification systems with demographic fairness considerations, there are two objectives to satisfy: 1) maximizing utility for the specific task and 2) ensuring fairness w.r.t. a known demographic attribute. These objectives often compete, so optimizing both can lead to a trade-off between utility and fairness. While existing works acknowledge the trade-offs and study their limits, two questions remain unanswered: 1) What are the optimal trade-offs between utility and fairness? and 2) How can we numerically quantify these trade-offs from data for a desired prediction task and demographic attribute of interest? This paper addresses these questions. We introduce two utility-fairness trade-offs: the Data-Space and Label-Space Trade-off. The trade-offs reveal three regions within the utility-fairness plane, delineating what is fully and partially possible and impossible. We propose U-FaTE, a method to numerically quantify the trade-offs for a given prediction task and group fairness definition from data samples. Based on the trade-offs, we introduce a new scheme for evaluating representations. An extensive evaluation of fair representation learning methods and representations from over 1000 pre-trained models revealed that most current approaches are far from the estimated and achievable fairness-utility trade-offs across multiple datasets and prediction tasks.
Abstract (translated)
在考虑人口公平性时构建分类系统时，有两个目标需要满足：1）最大化特定任务的效用，2）确保已知人口属性的公平性。这两个目标通常会竞争，因此优化这两个目标可能会导致效用和公平性的权衡。尽管现有的工作承认这些权衡并研究其局限性，但两个问题仍然未得到回答：1）效用和公平性之间的最优权衡是什么？2）我们如何从数据中数值量化这些权衡，以便为所感兴趣的预测任务和人口属性制定预测？本文回答了这些问题。我们引入了两种效用-公平性权衡：数据空间和标签空间权衡。权衡揭示了效用-公平性平面上的三个区域，区分了完全和部分可能性和不可能的情况。我们提出了U-FaTE，一种从数据样本中数值量化权衡的方法，用于特定预测任务和人口定义。基于权衡，我们引入了一种新的评估表示的方案。对超过1000个预训练模型的公平表示学习方法和表示的深入评估表明，大多数现有方法离预计和可实现公平-效用权衡相差很远。
URL
https://arxiv.org/abs/2404.09454
