Fair representation learning (FRL) is a popular class of methods aiming to produce fair classifiers via data preprocessing. Recent regulatory directives stress the need for FRL methods that provide practical certificates, i.e., provable upper bounds on the unfairness of any downstream classifier trained on preprocessed data, which directly provides assurance in a practical scenario. Creating such FRL methods is an important challenge that remains unsolved. In this work, we address that challenge and introduce FARE (Fairness with Restricted Encoders), the first FRL method with practical fairness certificates. FARE is based on our key insight that restricting the representation space of the encoder enables the derivation of practical guarantees, while still permitting favorable accuracy-fairness tradeoffs for suitable instantiations, such as one we propose based on fair trees. To produce a practical certificate, we develop and apply a statistical procedure that computes a finite sample high-confidence upper bound on the unfairness of any downstream classifier trained on FARE embeddings. In our comprehensive experimental evaluation, we demonstrate that FARE produces practical certificates that are tight and often even comparable with purely empirical results obtained by prior methods, which establishes the practical value of our approach.

翻译：公平表示学习（FRL）是一类旨在通过数据预处理生成公平分类器的流行方法。最近的监管指令强调，FRL方法需提供实用证书，即对基于预处理数据训练的任何下游分类器的不公平性给出可证明的上界，从而在实际场景中直接提供保障。创建此类FRL方法是一项尚未解决的重要挑战。在本工作中，我们应对这一挑战，并引入FARE（受限编码器公平性），这是首个具备实用公平证书的FRL方法。FARE基于我们的核心见解：限制编码器的表示空间能够推导出实用保障，同时允许通过合适的实例化（如我们提出的基于公平树的方案）实现良好的精度-公平性权衡。为生成实用证书，我们开发并应用了一种统计程序，该程序可计算基于FARE嵌入训练的任何下游分类器不公平性的有限样本高置信度上界。在全面的实验评估中，我们证明FARE生成的实用证书紧致，甚至常与先前方法获得的纯经验结果相当，这确立了本方法的实用价值。