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