Fairness of Ensemble Classifiers on Lending Data

Building on prior work from the lab on fairness composition in ensemble machine learning, this project evaluates ensemble classifiers across several public lending datasets, including HMDA mortgage data, Adult Census, Taiwan Credit, and German Credit.

The goal is to understand how well the fairness and accuracy tradeoffs reported on benchmark datasets carry over to realistic lending data.