Fairness in Multi-Task Learning via Wasserstein Barycenters
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866916090704035840 |
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| author | Hu, François Ratz, Philipp Charpentier, Arthur |
| author_facet | Hu, François Ratz, Philipp Charpentier, Arthur |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_10155 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fairness in Multi-Task Learning via Wasserstein Barycenters Hu, François Ratz, Philipp Charpentier, Arthur Machine Learning Computers and Society 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. |
| title | Fairness in Multi-Task Learning via Wasserstein Barycenters |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2306.10155 |