Fairness by Design: Machine Learning and Interpretable Mortgage Lending

Regulated lenders must explain denials and avoid disparate treatment in U.S. mortgages, yet flexible scoring can be opaque. We develop and study a less discriminatory and transparent machine-learning approach that embeds equalized-odds fairness in estimation and decomposes each decision into feature contributions suitable for adverse-action notices. Using HMDA applications, the model preserves performance while shrinking minority–nonminority error gaps and reweighting away from geographic proxies toward core underwriting signals (debt-to-income, loan-to-value). Supplementary analyses around underwriting cutoffs and lender-level tests indicate that the model lowers minority shortfalls at the margin and reduces within-lender disparities.