Discrimination on Wheels: How Big Data Uses License Plate Surveillance to Put the Brakes on Disadvantaged Drivers

64 Pages Posted: 5 May 2021 Last revised: 1 Jun 2022

See all articles by Nicole McConlogue

Nicole McConlogue

West Virginia University College of Law

Date Written: 2021


As scholarly discourse increasingly raises concerns about the negative societal effects of “fintech,” “dirty data,” and “technochauvinism,” a growing technology provides an instructive illustration of these concepts. Surveillance software companies develop predictive analytical tools based on automated license plate reader (ALPR) technology and market the tools to auto financers and insurers as a risk assessment input when evaluating consumer applicants. Proponents might argue that more information about consumer travel habits results in more accurate and individualized risk predictions, potentially increasing vehicle ownership among marginalized groups. Expanding access to cars would go a long way toward undoing the economic hobbling of many people who are low-income or of color.

However, identifying and observing discrimination’s entry points in the consumer scoring cycle shows ALPR-based data analytics will only exacerbate the problem. Competing incentives and assumptions steer the choices of the humans who collect ALPR data, creating a conflict that irredeemably poisons the data and any consumer access decisions that spring from it. Moreover, using location data to assess risk means that automobile costs are based on value judgments about the neighborhoods consumers visit. Thus, not only does the tainted ALPR data collection methodology reinforce discrimination rather than creating an equal path to economic mobility and stability, but using the data to score consumers affirmatively resuscitates and repackages the practice of redlining.

This article analyzes the fintech model as represented by ALPR’s application to the landscape of auto financing and insurance. This article deviates from other commentary surrounding ALPR by contemplating this technology specifically through a consumer law lens: a context overlooked by a conversation preoccupied with ALPR’s privacy and Fourth Amendment implications. Even as scholars and commentators examine law enforcement’s engagement with this high-tech surveillance, powerful private actors fly under the radar while subjecting vulnerable consumers to ALPR’s exploitative commercial applications. It exposes the ways in which consumer laws have left disadvantaged drivers unprotected and advances a number of proposals, including removing geographic inputs from auto access decision making, developing a central base of technological expertise to audit algorithms, and banning commercial use of ALPR.

By permission of the Board of Trustees of the Leland Stanford Junior University, from the Stanford Journal of Civil Rights and Civil Liberties at Volume 18, page 269 (2022). For information visit: https://law.stanford.edu/stanford-journal-of-civil-rights-civil-liberties-sjcrcl/.

Keywords: big data, data analysis, surveillance, predictive analysis, ALPR, automated license plate reader, consumer law, consumer protection, discrimination, fintech, auto financing, auto insurance

JEL Classification: K10

Suggested Citation

McConlogue, Nicole, Discrimination on Wheels: How Big Data Uses License Plate Surveillance to Put the Brakes on Disadvantaged Drivers (2021). WVU College of Law Research Paper, 2021-009, Stanford Journal of Civil Rights and Civil Liberties, Vol. 18 (2022)., Available at SSRN: https://ssrn.com/abstract=3839726

Nicole McConlogue (Contact Author)

West Virginia University College of Law ( email )

101 Law School Drive
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United States
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26506-7914 (Fax)

HOME PAGE: http://https://www.law.wvu.edu/faculty-staff/faculty/nicole-mcconlogue

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