Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, and fairness could be violated when there is a modest number of samples, which is often the case in practice. In this paper, we propose FaiREE, a fair classification algorithm that can satisfy group fairness constraints with finite-sample and distribution-free theoretical guarantees. FaiREE can be adapted to satisfy various group fairness notions (e.g., Equality of Opportunity, Equalized Odds, Demographic Parity, etc.) and achieve the optimal accuracy. These theoretical guarantees are further supported by experiments on both synthetic and real data. FaiREE is shown to have favorable performance over state-of-the-art algorithms.

翻译：算法公平性在机器学习研究中扮演着日益重要的角色。目前已提出多种群体公平性定义及相应算法。然而，现有公平分类方法的公平性保证主要依赖于特定的数据分布假设，通常需要大样本量支持，当样本量适中时（实践中常见情况）公平性可能被破坏。本文提出FaiREE算法，一种能够在有限样本且无需分布假设的理论保证下满足群体公平性约束的公平分类方法。FaiREE可适配多种群体公平性准则（如机会均等、均衡几率、人口统计均等），并实现最优准确率。这些理论保证通过合成数据与真实数据的实验得到进一步验证。实验表明，FaiREE相较于现有最优算法具有更优越的性能。