# Minimax Pareto Fairness: A Multi Objective Perspective

**Authors:** Natalia Martinez, Martin Bertran, Guillermo Sapiro  
**arXiv:** [2011.01821](https://arxiv.org/abs/2011.01821)  
**Date:** 2020-11-03  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2011.01821v1/

## Abstract

In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where a classifier achieves minimax risk and is Pareto-efficient w.r.t. all groups, avoiding unnecessary harm, and can lead to the best zero-gap model if policy dictates so. We provide a simple optimization algorithm compatible with deep neural networks to satisfy these constraints. Since our method does not require test-time access to sensitive attributes, it can be applied to reduce worst-case classification errors between outcomes in unbalanced classification problems. We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk, demonstrating how our framework compares favorably to other approaches.