Fairness in Multi-Task Learning via Wasserstein Barycenters

• URL: http://arxiv.org/abs/2306.10155v2
• Date: Thu, 6 Jul 2023 09:37:36 GMT
• Title: Fairness in Multi-Task Learning via Wasserstein Barycenters
• Authors: Fran\c{c}ois Hu, Philipp Ratz, Arthur Charpentier
• Abstract summary: Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. We develop a method that extends the definition of Strong Demographic Parity to multi-task learning using multi-marginal Wasserstein barycenters. Our approach provides a closed form solution for the optimal fair multi-task predictor including both regression and binary classification tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. Recent advances have proposed various methods to ensure fairness in a univariate environment, where the goal is to de-bias a single task. However, extending fairness to a multi-task setting, where more than one objective is optimised using a shared representation, remains underexplored. To bridge this gap, we develop a method that extends the definition of Strong Demographic Parity to multi-task learning using multi-marginal Wasserstein barycenters. Our approach provides a closed form solution for the optimal fair multi-task predictor including both regression and binary classification tasks. We develop a data-driven estimation procedure for the solution and run numerical experiments on both synthetic and real datasets. The empirical results highlight the practical value of our post-processing methodology in promoting fair decision-making.

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