Post-processing mitigation techniques for group fairness generally adjust the decision threshold of a base model in order to improve fairness. Methods in this family exhibit several advantages that make them appealing in practice: post-processing requires no access to the model training pipeline, is agnostic to the base model architecture, and offers a reduced computation cost compared to in-processing. Despite these benefits, existing methods face other challenges that limit their applicability: they require knowledge of the sensitive attributes at inference time and are oftentimes outperformed by in-processing. In this paper, we propose a general framework to transform any in-processing method with a penalized objective into a post-processing procedure. The resulting method is specifically designed to overcome the aforementioned shortcomings of prior post-processing approaches. Furthermore, we show theoretically and through extensive experiments on real-world data that the resulting post-processing method matches or even surpasses the fairness-error trade-off offered by the in-processing counterpart.

翻译：群体公平的后处理缓解技术通常通过调整基模型的决策阈值来提升公平性。这类方法在实际应用中展现出若干优势：无需访问模型训练流程，独立于基模型架构，且相较于过程内处理方法计算成本更低。尽管具备这些优点，现有方法仍面临其他限制其适用性的挑战：在推理时需获取敏感属性信息，且性能往往劣于过程内处理方法。本文提出一种通用框架，可将任何具有惩罚目标的过程内处理方法转化为后处理流程。所提方法专为克服前述后处理方法的缺陷而设计。此外，通过理论证明和在真实数据上的大量实验表明，该后处理方法的公平性-误差权衡效果可与过程内处理方法相媲美甚至更优。