Journal article
Multi-Output Distributional Fairness via Post-Processing
Transactions on Machine Learning Research, Vol.2025(April), pp.1-21
04/04/2025
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 methods for achieving distributional parity rely on the (inverse) cumulative density function of a model’s output, restricting their applicability to single-output models. Extending previous works, we propose to employ optimal transport mappings 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 to extend this process to out-of-sample data. Our empirical studies evaluate the proposed approach against various baselines on multi-task/multi-class classification and representation learning tasks, demonstrating the effectiveness of the proposed approach.
Details
- Title: Subtitle
- Multi-Output Distributional Fairness via Post-Processing
- Creators
- Gang Li - Texas A&M UniversityQihang Lin - University of IowaAyush Ghosh - University of IowaTianbao Yang - Texas A&M University
- Resource Type
- Journal article
- Publication Details
- Transactions on Machine Learning Research, Vol.2025(April), pp.1-21
- ISSN
- 2835-8856
- eISSN
- 2835-8856
- Grant note
- 2147253 / National Science Foundation (100000001) National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001) 2147253 / National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001)
- Language
- English
- Date published
- 04/04/2025
- Academic Unit
- Computer Science; Business Analytics
- Record Identifier
- 9984825636802771
Metrics
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