The Moral Limits of Predictive Practices: The Case of Credit-Based Insurance Scores

Barbara Kiviat, PhD candidate in Sociology and Social Policy, Harvard University

Corporations increasingly gather massive amounts of consumer data to predict how individuals will behave so that they can more profitably price goods and allocate resources like insurance, credit, and jobs. This paper investigates the moral foundations of such predictive allocation. I leverage the case of credit scores in car insurance pricing—an early and controversial use of algorithms in the U.S. consumer economy—to understand how mathematical prediction functions as a framework of market fairness and the ways people push back against it. Drawing on the sociology of quantification, I theorize the features of numbers that make it seem that companies are simply giving consumers what they deserve. I then use an in-depth qualitative case study of policymaker resistance to credit-based insurance scores to show how the moral power of numbers can be undone. This study advances economic sociology by demonstrating that social actors use moral arguments not only to resist marketization full stop, but also to make fine-grained normative distinctions within market rationality. As “big data” and predictive analytics permeate markets of all sorts, as well as other domains of social life, these findings carry implications for how sociologists approach the novel forms of stratification that result.