Minimax Pareto Fairness: A Multi Objective Perspective

• URL: http://arxiv.org/abs/2011.01821v1
• Date: Tue, 3 Nov 2020 16:21:53 GMT
• Title: Minimax Pareto Fairness: A Multi Objective Perspective
• Authors: Natalia Martinez, Martin Bertran, Guillermo Sapiro
• Abstract summary: Group fairness is a multi-objective optimization problem, where each sensitive group risk is a separate objective. We provide a simple algorithm compatible with deep neural networks to satisfy these constraints. We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk.
• Score: 24.600419295290504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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