Abstract:
What mechanisms enable the expansion of algorithmic scoring? While extant explanations emphasize organizational and political dynamics, such accounts offer conflicting intuition on the consequences of algorithmic scoring for racial inequality. As a result, we are unable to reconcile a growing phenomenon where the expansion of scoring and data used is resonating with political actors as a way to promote racial inclusion in the face of claims to the contrary. To address this, I develop an account of algorithmic expansion rooted in how political and legal institutions cognize inequality. I analyze the history of consumer credit scoring from 1968-2019 and demonstrate how a particular model of conceptualizing and empirically identifying discrimination emerged, was institutionalized, and had the consequence of enabling the acceptance of credit scores as “race neutral.” I argue the ability to legitimately identify credit scoring as unrelated to race cleared the way for algorithmic expansion as scoring could easily dispatch critiques of being racially unjust despite pervasive racial inequality. By shifting the focus away from whether algorithms perpetuate inequalities to understanding the epistemological infrastructure shaping how institutions come to know the answer, this article yields new theoretical and normative insight into inequality in the algorithmic age.
A Brief Bio:
Davon Norris is an Assistant Professor of Organizational Studies and Sociology (by courtesy) and faculty associate at the Stone Center for Inequality Dynamics at the University of Michigan. His research lies at the intersection of economic sociology, urban sociology, and the study of race/racism and seeks to understand how credit, debt, and finance shape patterns of inequality.