Protecting the Protected Group: Circumventing Harmful Fairness

Machine Learning (ML) algorithms shape our lives. Banks use them to determine if we are good borrowers; IT companies delegate them recruitment decisions; police apply ML for crime-prediction, and judges base their verdicts on ML. However, real-world examples show that such automated decisions tend to discriminate protected groups. This generated a huge hype both in media and in the research community. Quite a few formal notions of fairness were proposed, which take a form of constraints a ``fair'' algorithm must satisfy. We focus on scenarios where fairness is imposed on a self-interested party (e.g., a bank that maximizes its revenue). We find that the disadvantaged protected group can be worse off after imposing a fairness constraint. We introduce a family of Welfare-Equalizing fairness constraints that equalize per-capita welfare of protected groups, and include Demographic Parity and Equal Opportunity as particular cases. In this family, we characterize conditions under which the fairness constraint helps the disadvantaged group. We also characterize the structure of the optimal Welfare-Equalizing classifier for the self-interested party, and provide an LP-based algorithm to compute it. Overall, our Welfare-Equalizing fairness approach provides a unified framework for discussing fairness in classification in the presence of a self-interested party.