A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction

ArXi:2603.18838v1 Announce Type: new 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.