A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction
The work proposes a model ensembling post-processing framework to achieve fairness-aware predictions. This method blends predictions from a complex pre-trained model and a simpler, fairness-adjusted model using an input-dependent weight. The framework is designed to enhance fairness across classification, regression, and survival analysis while maintaining high predictive accuracy.