标题： 多输出分布公平性通过后处理实现 显示英文标题

标题： Multi-Output Distributional Fairness via Post-Processing

摘要： 后处理方法由于直观性、较低的计算成本和出色的可扩展性，正成为提升机器学习模型公平性的突出技术。然而，大多数现有的后处理方法是为特定任务的公平性度量而设计的，并且仅限于单输出模型。 本文介绍了一种用于多输出模型的后处理方法，例如用于多任务/多类别分类和表征学习的模型，以增强模型的分布公平性，这是一种与任务无关的公平性度量。 现有实现分布公平性的方法依赖于模型输出的（逆）累积密度函数，限制了它们在单输出模型中的适用性。 通过扩展先前的工作，我们提出使用最优传输映射将模型的输出在不同组之间移动到其经验Wasserstein质心。应用了一种近似技术来降低计算精确质心的复杂度，并提出了核回归方法以将此过程扩展到样本外数据。 我们的实证研究在多任务/多类别分类和表征学习任务上评估了所提出的方法与各种基线的对比，展示了所提出方法的有效性。 显示英文摘要

摘要： 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.