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


Wednesday, September 19, 2018, 4:00pm to 5:30pm


William James Hall, 33 Kirkland Street, Room 1550


Economic Sociology Seminar presentation by 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