As machine learning (ML) based systems are adopted in domains such as law enforcement, criminal justice, finance, hiring and admissions, ensuring the fairness of ML aided decision-making is becoming increasingly important. In this paper, we focus on the problem of fair classification, and introduce a novel min-max F-divergence regularization framework for learning fair classification models while preserving high accuracy. Our framework consists of two trainable networks, namely, a classifier network and a bias/fairness estimator network, where the fairness is measured using the statistical notion of F-divergence. We show that F-divergence measures possess convexity and differentiability properties, and their variational representation make them widely applicable in practical gradient based training methods. The proposed framework can be readily adapted to multiple sensitive attributes and for high dimensional datasets. We study the F-divergence based training paradigm for two types of group fairness constraints, namely, demographic parity and equalized odds. We present a comprehensive set of experiments for several real-world data sets arising in multiple domains (including COMPAS, Law Admissions, Adult Income, and CelebA datasets). To quantify the fairness-accuracy tradeoff, we introduce the notion of fairness-accuracy receiver operating characteristic (FA-ROC) and a corresponding \textit{low-bias} FA-ROC, which we argue is an appropriate measure to evaluate different classifiers. In comparison to several existing approaches for learning fair classifiers (including pre-processing, post-processing and other regularization methods), we show that the proposed F-divergence based framework achieves state-of-the-art performance with respect to the trade-off between accuracy and fairness.

翻译：随着基于机器学习的系统在执法、刑事司法、金融、招聘和招生等领域的应用，确保机器学习辅助决策的公平性正变得日益重要。本文聚焦于公平分类问题，提出了一种新颖的最小-最大F散度正则化框架，用于在保持高准确率的同时学习公平分类模型。该框架包含两个可训练的网络，即分类器网络和偏差/公平性估计网络，其中公平性采用F散度这一统计概念进行度量。我们表明F散度度量具有凸性和可微性，且其变分表示使其能够广泛适用于基于梯度的实际训练方法。所提框架可便捷地适应多个敏感属性和高维数据集。我们研究了基于F散度的训练范式，针对两类群体公平性约束——人口统计均等和均等化几率。针对多个领域（包括COMPAS、Law Admissions、Adult Income和CelebA数据集）的若干真实世界数据集，我们开展了一组全面的实验。为量化公平性与准确率之间的权衡，我们引入了公平-准确率受试者工作特征（FA-ROC）及其对应的低偏置FA-ROC概念，认为这是评估不同分类器的恰当指标。与现有多种公平分类器学习方法（包括预处理、后处理及其他正则化方法）相比，我们表明所提出的基于F散度的框架在准确率与公平性的权衡方面达到了最先进的性能。