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CITP Seminar: The Social Production of “Accurate” Personal Financial Data

Date and Time
Tuesday, March 21, 2023 - 12:30pm to 1:30pm
Location
Sherrerd Hall 306
Type
CITP
Speaker
Jordan Brensinger, from Columbia University

Jordan Brensinger
In the United States, financial institutions leverage personal data for countless decisions impacting individual wellbeing, ranging from managing access to existing accounts to deciding who to offer credit and on what terms. The legitimacy, and arguably the efficacy, of those decision-making processes therefore hinges partly on the accuracy of the underlying personal data. But what does it mean for personal financial data to be “accurate?” Technical and legal definitions often define data accuracy as an objective quality of the information—it either reflects the correct value, or it does not—thereby merely replacing accuracy with other ambiguous terms like correctness. We are left with the questions: what counts as “correct,” and how are such determinations made? In this paper, a phenomenological account of data accuracy is offered.

Drawing on an original qualitative study of identity theft resolution—including (1) over a hundred interviews with victims and organizational personnel, (2) observations in financial industry and nonprofit advocacy settings, and (3) analysis of organizational and legal documents—it is argued that “accurate” personal financial data is the product of a sociotechnical process of negotiation between potentially conflicting subjectivities. Yet the law empowers financial institutions to adjudicate these disputes with consumers, tilting the negotiation table against the latter in ways that often reinforce existing marginalities. Disputes thus serve to alert institutions to potential gaps between data and data subjects without fundamentally challenging their financial interest—thereby shoring up the legitimacy, if not always the efficacy, of consumer surveillance.

Bio: Harnessing qualitative and quantitative methods, Jordan Brensinger’s research primarily explores how organizations uniquely identify people and the social, economic, and policy implications of those processes.

In his dissertation, Brensinger investigated consumer identification through a multi-site, multi-method study of financial identity theft—including interviews with victims and professionals; observations at industry events, a nonprofit call center, and the fraud department of a large credit union; and analysis of organizational and policy documents. By juxtaposing organizational expertise and techniques with consumer accounts of data disputes, his dissertation documented the sociotechnical work that goes into constructing “accurate” personal data and consumer identities while revealing how personal data generates new forms of labor and insecurity for the public as they go about their everyday lives. Insights from this project have been presented at conferences like the Annual Meeting of the American Sociological Association (ASA), the Privacy Law Scholars Conference (PLSC), and the Society for the Advancement of Socio Economics (SASE), as well as published in Sociological Theory.

Brensinger’s work leverages partnerships with public, private, and non-profit organizations—including federal agencies, financial institutions, and legal aid offices—to generate compelling questions, amplify on-the-ground expertise, and produce actionable insights. These partnerships have so far produced internal reports, presentations to practitioner groups, and a coauthored thought leadership piece on data justice.

Brensinger completed his Ph.D. in sociology from Columbia University, where he also earned Master’s degrees in sociology and urban and social policy. His intellectual outlook and commitment to seeking a more just and equitable world also derive from extensive experience living, working, and learning on five continents.


To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

This seminar will not be recorded.

Watch the webinar here.

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