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关键的角色。尽管已有多种群体公平性概念和算法被提出，但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，且通常需要大样本量。当样本数量有限时（这在实践中屡见不鲜），公平性可能遭到破坏。本文提出FaiREE算法，该公平分类算法能够在有限样本条件下提供无分布假设的理论保证，满足群体公平性约束。FaiREE可适配多种群体公平性概念（如机会均等、均等化几率、人口统计均等性等），并实现最优精度。这些理论保证通过合成数据和真实数据的实验得到进一步验证。实验表明，FaiREE的性能优于当前最先进算法。