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

• URL: http://arxiv.org/abs/2603.18838v1
• Date: Thu, 19 Mar 2026 12:38:43 GMT
• Title: A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction
• Abstract summary: We propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling.<n>We demonstrate that the framework effectively enhances fairness while maintaining, or only minimally affecting, predictive accuracy.
• Score: 0.49273070292463234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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