Striking an optimal balance between predictive performance and fairness continues to be a fundamental challenge in machine learning. In this work, we propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling. Designed to operate independently of any specific model internals, our approach is widely applicable across various learning tasks, model architectures, and fairness definitions. Through extensive experiments spanning classification, regression, and survival analysis, we demonstrate that the framework effectively enhances fairness while maintaining, or only minimally affecting, predictive accuracy.

翻译：在机器学习中，在预测性能与公平性之间实现最优平衡仍是一项基本挑战。本文提出一种后处理框架，通过利用模型集成来实现公平感知预测。该框架设计独立于任何特定模型内部结构，可广泛适用于各类学习任务、模型架构及公平性定义。通过在分类、回归和生存分析任务上的大量实验，我们证明该框架在维持预测准确率仅受极小影响的同时，能有效提升公平性。