Abstract Fair lending’s disparate impact doctrine aims to address lending disparities. But which disparities? Traditional fair lending has narrowly focused on equal outcomes—examining differences in loan approval rates or interest rates. However, this singular focus overlooks other dimensions of disparities that are essential for fair credit access. This article challenges the conventional emphasis on equal outcomes, demonstrating how it has failed to address deep-rooted inequalities in traditional credit allocation while also stifling innovation in machine-learning and alternative data. We argue that disparities in the validity of creditworthiness predictions—the accuracy with which a model identifies creditworthy applicants—importantly impact equal access to credit and, in particular, the extension of credit to the creditworthy. Despite mounting empirical evidence of the harm of validity disparities, traditional fair lending enforcement inadequately recognizes this disparity dimension, a gap that may become increasingly harmful as lending decisions rely on advanced statistical methods. Future regulatory guidance, enforcement, and supervision should explicitly recognize validity inequalities across protected groups while addressing the accompanying challenges of this more comprehensive perspective on disparities, which is essential for equitable credit allocation.
Caro et al. (Tue,) studied this question.
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