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

2026-03-19

AI Summary

This work proposes a general, model-agnostic post-processing framework that systematically integrates ensemble learning into fairness optimization. By aggregating predictions from multiple base models without requiring access to their internal architectures, the approach uniformly supports diverse predictive tasks—including classification, regression, and survival analysis—and accommodates a wide range of fairness definitions. The method addresses the inherent trade-off between predictive performance and fairness in machine learning models. Experimental results demonstrate that it significantly enhances fairness while maintaining or only marginally compromising prediction accuracy, thereby validating its effectiveness and generalizability across multiple scenarios.

Technology Category

• Machine Learning: Ensemble Methods
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• fairness
• predictive performance
• machine learning
• model fairness
• prediction accuracy

Innovation

• model ensemble
• post-processing
• fairness-aware prediction
• predictive performance
• fairness