Multi-Output Distributional Fairness via Post-Processing

• URL: http://arxiv.org/abs/2409.00553v1
• Date: Sat, 31 Aug 2024 22:41:26 GMT
• Title: Multi-Output Distributional Fairness via Post-Processing
• Authors: Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang,
• Abstract summary: We introduce a post-processing method for multi-output models to enhance a model's distributional parity, a task-agnostic fairness measure. Our method employs an optimal transport mapping to move a model's outputs across different groups towards their empirical Wasserstein barycenter.
• Score: 47.94071156898198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The post-processing approaches are becoming prominent techniques to enhance machine learning models' fairness because of their intuitiveness, low computational cost, and excellent scalability. However, most existing post-processing methods are designed for task-specific fairness measures and are limited to single-output models. In this paper, we introduce a post-processing method for multi-output models, such as the ones used for multi-task/multi-class classification and representation learning, to enhance a model's distributional parity, a task-agnostic fairness measure. Existing techniques to achieve distributional parity are based on the (inverse) cumulative density function of a model's output, which is limited to single-output models. Extending previous works, our method employs an optimal transport mapping to move a model's outputs across different groups towards their empirical Wasserstein barycenter. An approximation technique is applied to reduce the complexity of computing the exact barycenter and a kernel regression method is proposed for extending this process to out-of-sample data. Our empirical studies, which compare our method to current existing post-processing baselines on multi-task/multi-class classification and representation learning tasks, demonstrate the effectiveness of the proposed approach.

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