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dc.contributor.advisorDobbin, Frank
dc.contributor.authorKiviat, Barbara Jo
dc.date.accessioned2019-12-12T08:57:06Z
dash.embargo.terms2021-05-01
dc.date.created2019-05
dc.date.issued2019-05-17
dc.date.submitted2019
dc.identifier.citationKiviat, Barbara Jo. 2019. Prediction and the Moral Order: Contesting Fairness in Consumer Data Capitalism. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42029685*
dc.description.abstractCorporations increasingly gather massive amounts of personal data to mathematically predict how individuals will behave and then treat people differently based on these predictions. This dissertation unpacks the moral foundations of such market arrangements by analyzing the case of U.S. car insurance pricing and regulation. I focus on a series of public policy debates about which sorts of data are fair for car insurers to use in deciding who to sell insurance to and what prices to charge, starting with the introduction of credit scores in the early 1990s and going through debates about “big data” today. To understand how people justify and contest the use of personal, predictive data, I draw on more than 13,000 pages of documents and 40 hours of recordings from regulatory and legislative hearings and investigations; 50 interviews with policymakers, members of industry, and other powerful actors; observations from more than a dozen regulatory events; and an original, nationally representative survey of 1,095 Americans. I advance the literature in economic sociology by showing that the idea of actuarial fairness—that data which predict are, by definition, fair to use—functions as a moral argument by obscuring why prediction works and what leads people to show up in the data the way they do. Policymakers and others who mobilize a competing conception of market fairness, one rooted in notions of moral deservingness, seek to recover such distinctions, which wind up shaping the law and regulation that follow.
dc.description.sponsorshipSocial Policy
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectinsurance
dc.subjectcar insurance
dc.subjecteconomic sociology
dc.subjectactuarial fairness
dc.subjectmorality
dc.subjectmarkets
dc.subjectcredit scores
dc.subjectpersonal data
dc.subjectbig data
dc.subjectregulation
dc.titlePrediction and the Moral Order: Contesting Fairness in Consumer Data Capitalism
dc.typeThesis or Dissertation
dash.depositing.authorKiviat, Barbara Jo
dash.embargo.until2021-05-01
dash.embargo.until2023-05-01
dc.date.available2019-12-12T08:57:06Z
thesis.degree.date2019
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.levelDoctoral
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
thesis.degree.nameDoctor of Philosophy
dc.contributor.committeeMemberBeckfield, Jason
dc.contributor.committeeMemberSmall, Mario L.
dc.type.materialtext
thesis.degree.departmentSocial Policy
thesis.degree.departmentSocial Policy
dash.identifier.vireo
dash.author.emailbarbarajkiviat@gmail.com


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