# Protecting the Protected Group: Circumventing Harmful Fairness

## TL;DR

This paper introduces Welfare-Equalizing fairness constraints to address discrimination in ML, showing how they can improve protected group welfare and providing algorithms for optimal classifiers in self-interested scenarios.

## Contribution

It proposes a unified Welfare-Equalizing fairness framework, generalizing existing notions like Demographic Parity and Equal Opportunity, and analyzes conditions for aiding disadvantaged groups.

## Key findings

- Welfare-Equalizing constraints can improve protected group welfare.
- The paper characterizes optimal classifiers under these fairness constraints.
- Algorithms for computing optimal classifiers are provided.

## Abstract

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 against protected groups. This potential discrimination 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 \textit{Welfare-Equalizing} fairness constraints that equalize per-capita welfare of protected groups, and include \textit{Demographic Parity} and \textit{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 \textit{Welfare-Equalizing} classifier for the self-interested party, and provide an algorithm to compute it. Overall, our \textit{Welfare-Equalizing} fairness approach provides a unified framework for discussing fairness in classification in the presence of a self-interested party.