Fair representation learning (FRL) is a popular class of methods aiming to produce fair classifiers via data preprocessing. However, recent work has shown that prior methods achieve worse accuracy-fairness tradeoffs than originally suggested by their results. This dictates the need for FRL methods that provide provable upper bounds on unfairness of any downstream classifier, a challenge yet unsolved. In this work we address this challenge and propose Fairness with Restricted Encoders (FARE), the first FRL method with provable fairness guarantees. Our key insight is that restricting the representation space of the encoder enables us to derive suitable fairness guarantees, while allowing empirical accuracy-fairness tradeoffs comparable to prior work. FARE instantiates this idea with a tree-based encoder, a choice motivated by inherent advantages of decision trees when applied in our setting. Crucially, we develop and apply a practical statistical procedure that computes a high-confidence upper bound on the unfairness of any downstream classifier. In our experimental evaluation on several datasets and settings we demonstrate that FARE produces tight upper bounds, often comparable with empirical results of prior methods, which establishes the practical value of our approach.
翻译:公平代表制学习(FRL)是通过数据预处理产生公平分类员的流行方法类别,然而,最近的工作表明,以往方法的准确性和公平性取舍比其结果的最初建议更差,这就要求FRL方法对任何下游分类员的不公平性提供可辨别的最高界限,这是一个挑战,但尚未解决。在这项工作中,我们处理这一挑战,并提议与受限制的分类员(FARE)公平性(FARE)(FRL)(这是第一个具有可辨别公平性保证的FRL方法)公平性。我们的主要见解是,限制编码员的代表权使我们能够获得适当的公平性保障,同时允许与先前工作相类似的经验性准确性-公平取舍。 FARE(FARE)将这一想法与基于树的编码器(一种基于决定树的内在优势的选择)一起快速地转化,而这种选择是我们在环境应用时受到决定树型分类员的固有优势所激发的。 从根本上说,我们制定并应用一种实用的统计程序,对任何下游分类员的不公平性进行高度信任性约束。 在对若干数据集和背景进行实验性评估时,我们证明FARELEAR产生较紧的上界限的价值。