Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Although researchers have proposed various ways of defining group fairness, most of them only focused on the immediate fairness, ignoring the long-term impact of a fair classifier under the dynamic scenario where each individual can improve its feature over time. Such dynamic scenarios happen in real world, e.g., college admission and credit loaning, where each rejected sample makes effort to change its features to get accepted afterwards. In this dynamic setting, the long-term fairness should equalize the samples' feature distribution across different groups after the rejected samples make some effort to improve. In order to promote long-term fairness, we propose a new fairness notion called Equal Improvability (EI), which equalizes the potential acceptance rate of the rejected samples across different groups assuming a bounded level of effort will be spent by each rejected sample. We analyze the properties of EI and its connections with existing fairness notions. To find a classifier that satisfies the EI requirement, we propose and study three different approaches that solve EI-regularized optimization problems. Through experiments on both synthetic and real datasets, we demonstrate that the proposed EI-regularized algorithms encourage us to find a fair classifier in terms of EI. Finally, we provide experimental results on dynamic scenarios which highlight the advantages of our EI metric in achieving the long-term fairness. Codes are available in a GitHub repository, see https://github.com/guldoganozgur/ei_fairness.

翻译：设计一个不歧视不同群体的公平分类器是机器学习中的一个重要问题。尽管研究者已提出多种定义群体公平性的方式，但大多数仅关注即时公平性，忽略了在每个人均可随时间改进自身特征的动态场景下，公平分类器所产生的长期影响。此类动态场景在现实世界中普遍存在，例如大学录取和信贷借贷，其中被拒绝的样本会努力改变自身特征以便后续被接受。在这种动态设定下，长期公平性应使不同群体中样本的特征分布在被拒绝样本付出一定改进努力后趋于平等。为促进长期公平性，我们提出一种名为“平等可改进性”（Equal Improvability，EI）的新型公平性概念，该概念假设每个被拒绝样本会付出有界水平的努力，从而平等化不同群体中被拒绝样本的潜在接受率。我们分析了EI的性质及其与现有公平性概念的联系。为找到满足EI要求的分类器，我们提出并研究了三种不同方法以求解EI正则化优化问题。通过在合成数据集和真实数据集上的实验，我们证明所提出的EI正则化算法有助于找到符合EI的公平分类器。最后，我们提供了动态场景下的实验结果，凸显了EI指标在实现长期公平性方面的优势。相关代码可在GitHub仓库中获取，参见https://github.com/guldoganozgur/ei_fairness。