iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making

Abstract--People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically basedon machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups. In contrast, the alternative paradigm of individual fairnesshas received relatively little attention, and this paper advances this less explored direction. The paper introduces a method for probabilistically mapping user records into a lowrank representationthat reconciles individual fairness and the utility of classifiers and rankings in downstream applications. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets. Our experiments show substantial improvements over the best prior work for this setting. This is a preprint of a full paper at ICDE 2019. Please cite the ICDE proceedings version. I. INTRODUCTION Motivation: People are rated, ranked and selected or not selected inan increasing number of online applications, towards algorithmic decisions based on machine learning models. Examples are approvals or denials of loans or visas, predicting recidivism for law enforcement, or rankings in job portals. As algorithmic decision making becomes pervasive in all aspects of our daily life, societal and ethical concerns [1, 6] are rapidly growing. A basic approach is to establish policies that disallow the inclusion of potentially discriminating attributes such as gender or race, and ensure that classifiers and rankings operate solely on task-relevant attributes such as job qualifications.