用模型集成提升预测公平性，不影响准确率。
A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction
- 通过模型集成实现公平性增强，不依赖具体模型结构。
- 在分类、回归、生存分析任务中均有效提升公平性。
- 适合需兼顾公平与精度的各类机器学习应用。
在机器学习中，如何平衡预测性能与公平性仍是根本挑战。本文提出一种基于模型集成的后处理框架，通过集成多个模型的预测结果，实现公平性感知的预测。该方法独立于具体模型内部结构，适用于多种学习任务、模型架构和公平性定义。在分类、回归和生存分析任务上进行的大量实验表明，该框架能有效提升公平性，同时保持或仅轻微影响预测准确性。
原文摘要 · Abstract (English)
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.
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