Algorithmic Lending Bias: Evaluating the Fairness of Historical Redlining in Loan Approvals

This paper investigates the persistent influence of historical redlining on modern AI algorithms used in real estate and loan approvals. Utilizing Home Mortgage Disclosure Act (HMDA) data, we uncover demographic biases in loan approval processes and track their evolution over time. Through the application of machine learning models and the bias detection and mitigation toolkit, we assess fairness using metrics such as statistical parity difference, disparate impact, and the Theil Index. Our analysis demonstrates the existence of discrimination, and shows that mitigation techniques, such as reweighting and domain knowledge inclusion, can significantly reduce disparities and promote equity in loan approvals across race, gender, and ethnicity. This study also highlights the necessity of addressing historical biases in training data to foster fairer algorithmic decision-making, while proposing practical solutions for improving fairness in AI-based lending systems.