From Banal Surveillance to Function Creep: Automated License Plate Recognition (ALPR) in Denmark

Abstract

This article discusses how Automated License Plate Recognition (ALPR) has been implemented in Denmark across three different sectors: parking, environmental zoning, and policing. ALPR systems are deployed as a configuration of cameras, servers, and algorithms of computer vision that automatically reads and records license plates of passing cars. Through digital ethnography and interviews with key stakeholders in Denmark, we contribute to the fields of critical algorithm and surveillance studies with a concrete empirical study on how ALPR systems are configured according to user-specific demands. Each case gives nuance to how ALPR systems are implemented: (1) how the seamless charging for a “barrier-free” parking experience poses particular challenges for customers and companies; (2) how environmental zoning enforcement through automated fines avoids dragnet data collection through customized design and regulation; and (3) how the Danish Police has widened its dragnet data collection with little public oversight and questionable efficacy. We argue that ALPR enacts a form of “banal surveillance” because such systems operate inconspicuously under the radar of public attention. As the central analytic perspective, banality highlights how the demand for increasing efficiency in different domains makes surveillance socially and politically acceptable in the long run. Although we find that legal and civic modes of regulation are important for shaping the deployment of ALPR, the potential for function creep is embedded into the very process of infrastructuring due to a lack of public understanding of these technologies. We discuss wider consequences of ALPR as a specific and overlooked instance of algorithmic surveillance, contributing to academic and public debates around the embedding of algorithmic governance and computer vision into everyday life.

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