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


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 all three of these problems. Surveillance software companies are using automated license plate reader (ALPR) technology to develop predictive analytical tools. In turn, software companies market those tools to auto financers and insurers as a risk assessment input to evaluate consumers seeking to buy a car. Proponents of this technology might argue that more information about consumer travel habits will result 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 suppression of many people who are low-income or of color.

However, discrimination in the consumer scoring cycle shows that ALPR-based data analytics will only exacerbate the economic and racial disparities in car ownership. Competing incentives and biased 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 may be based on value judgments about the neighborhoods that consumers visit. Thus, rather than creating an equal path to economic mobility, the tainted ALPR data collection methodology reinforces discrimination. Not only that, but using the data to score consumers risks resuscitating and repackaging the practice of redlining.

This article analyzes the fintech model as represented by the use of ALPR technology in auto financing and insurance. Existing commentary surrounding ALPR has focused on ALPR’s privacy and Fourth Amendment implications. While scholars and commentators have been busy examining law enforcement’s engagement with this high-tech surveillance technology, powerful private actors have flown under the radar while subjecting vulnerable consumers to ALPR’s exploitative commercial applications. This article deviates from prior commentary by contemplating ALPR through a consumer law lens. It exposes the ways in which consumer laws have left disadvantaged drivers unprotected. Finally, it 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.


Stanford University Stanford, California
  • Nicole K. McConlogue, Discrimination on Wheels: How Big Data Uses License Plate Surveillance to Put the Brakes on Disadvantaged Drivers, 18 Stan. J. C.R. & C.L. 279 (2022).
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