The Price of Bias: A Study of Gender Bias Mitigation Techniques in Financial Loan Decision-Making

Abstract

As machine learning becomes increasingly used in financial decision-making, concerns about algorithmic fairness, particularly regarding gender bias, are growing. This thesis evaluates the effectiveness of four common bias mitigation techniques - Reweighing, Learning Fair Representations (LFR), Equality of Odds, and Reject Option-Based Classification - across multiple supervised learning models and under two distinct gender regimes. Rather than use a dataset that is uniformly biased, this study uses real-world HMDA data to investigate how different definitions of fairness may reveal or obscure bias. The study finds that while predictive accuracy remains relatively stable, withholding gender as a variable often improves both accuracy and fairness outcomes. Notably, debiasing techniques produce mixed results: Reweighing and Equality of Odds reduce difference in means (DIM) in outcome significantly, but standard fairness metrics often remain unchanged, raising questions about the adequacy of current fairness definitions. This study also finds that the tested models replicate the original discrepancies in loan approval rates between men and women, and the debiasing techniques are largely unable to improve this gap. These findings highlight the need for lenders to consider multiple fairness dimensions beyond widely accepted metrics and suggest further research into more holistic definitions of fairness in machine learning.