A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction
This work addresses fairness-aware prediction for machine learning practitioners, but it is incremental as it builds on existing post-processing and ensembling methods.
The paper tackled the challenge of balancing predictive performance and fairness in machine learning by proposing a model ensemble-based post-processing framework, demonstrating through experiments that it effectively enhances fairness while maintaining or minimally affecting accuracy across classification, regression, and survival analysis tasks.
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.