Multi-Output Distributional Fairness via Post-Processing
<|reference_start|>Multi-Output Distributional Fairness via Post-Processing: 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.<|reference_end|>
@article{li2024multi-output,
    title={Multi-Output Distributional Fairness via Post-Processing},
    author={Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang},
    journal={arXiv preprint arXiv:2409.00553},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00553},
    primaryClass={cs.LG cs.AI cs.CY stat.ML}
}