Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model. In these situations, post-processing is a viable alternative. However, current methods are tailored to specific problem settings and fairness definitions and hence, are not as broadly applicable as in-processing. In this work, we propose a framework that turns any regularized in-processing method into a post-processing approach. This procedure prescribes a way to obtain post-processing techniques for a much broader range of problem settings than the prior post-processing literature. We show theoretically and through extensive experiments that our framework preserves the good fairness-error trade-offs achieved with in-processing and can improve over the effectiveness of prior post-processing methods. Finally, we demonstrate several advantages of a modular mitigation strategy that disentangles the training of the prediction model from the fairness mitigation, including better performance on tasks with partial group labels.

翻译：尽管在公平性与误差权衡方面取得了有前景的结果，但针对群体公平性的处理中缓解技术在许多实际应用中无法使用，这些应用通常计算资源有限或无法访问预测模型的训练流程。在这些情况下，后处理成为一种可行的替代方案。然而，现有方法通常针对特定问题设置和公平性定义进行定制，因此其适用范围不如处理中方法广泛。在本研究中，我们提出一个框架，可将任何正则化的处理中方法转化为后处理方法。该框架为更广泛的问题设置提供了后处理技术的实现路径，其覆盖范围远超先前的后处理文献。我们通过理论分析和大量实验证明，该框架能够保持处理中方法所实现的良好公平性-误差权衡，并能提升现有后处理方法的有效性。最后，我们展示了模块化缓解策略的若干优势——该策略将预测模型训练与公平性缓解解耦，包括在部分群体标签任务中取得更优性能。