Profit-Based Audit Exposes Miscalibration in Fintech Lending Algorithms
A landmark study reveals how algorithmic lending platforms inadvertently create disparities despite excluding protected attributes. By analyzing 80,000 personal loans, researchers discovered systemic miscalibration in credit risk models that underestimated risk for Black borrowers and overestimated it for women, creating a fairness paradox with profound industry implications.