  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.
