Grant

Prediction and the moral order

Project Summary:

A structural change in the United States economy—huge new flows of personal information stemming from technological innovation—has enabled companies to classify, sort and rank individuals in ways previously unimaginable. This research proposes to use big data from car insurance providers to predict market decisions by looking at how regulators, members of industry and other key actors together establish the market rules by which personal data determines economic opportunity. It asks on what grounds policy and market actors conclude that it is fair to treat people differently in the marketplace based on their personal data “traces,” and seeks to show how some, but not other, ideas get embedded in markets over time.

Biography

Barbara Kiviat is a Ph.D. student in Sociology and Social Policy. Kiviat holds a B.A. in The Writing Seminars from Johns Hopkins University, an M.A. in journalism from Columbia University, and an M.P.A. from New York University, where she was a David Bohnett Public Service Fellow. As a research associate at NYU's Financial Access Initiative, Kiviat helped launch the U.S. Financial Diaries, a longitudinal study of the economic lives of 300 American families. Previously, Kiviat was a staff writer at Time magazine. She has also written for Fortune, Money, The Miami Herald, The Arizona Republic, The Chronicle of Higher Education, Reuters, and TheAtlantic.com, among other outlets. Her research interests revolve around household finance and public policy.