Achieving Fair Treatment in Algorithmic Classification
Fairness in classification has become an increasingly relevant and controversial issue as computers replace humans in many of today’s classification tasks. In particular, a subject of much recent debate is that of finding, and subsequently achieving, suitable definitions of fairness in an algorithmic context. In this work, following the work of Hardt et al. (NIPS’16), we consider and formalize the task of sanitizing an unfair classifier \(\mathcal {C}\) into a classifier \(\mathcal {C}'\) satisfying an approximate notion of “equalized odds” or fair treatment. Our main result shows how to take any (possibly unfair) classifier \(\mathcal {C}\) over a finite outcome space, and transform it—by just perturbing the output of \(\mathcal {C}\)—according to some distribution learned by just having black-box access to samples of labeled, and previously classified, data, to produce a classifier \(\mathcal {C}'\) that satisfies fair treatment; we additionally show that our derived classifier is near-optimal in terms of accuracy. We also experimentally evaluate the performance of our method.