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

The paper introduces a post‑processing framework that uses model ensembling to achieve fairness‑aware predictions. It is designed to work independently of any specific model internals, making it applicable to a wide range of learning tasks, model architectures, and fairness definitions. Experiments on classification, regression, and survival analysis show that the framework improves fairness while preserving predictive accuracy.

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