Local Law Enforcement Jumps on the Big Data Bandwagon: Automated License Plate Recognition Systems, Information Privacy, and Access to Government Information
42 Pages Posted: 19 Oct 2013 Last revised: 12 Jun 2014
Date Written: October 16, 2013
As government agencies and law enforcement departments increasingly adopt big-data surveillance technologies as part of their routine investigatory practice, personal information privacy concerns are becoming progressively more palpable. On the other hand, advancing technologies and data-mining potentially offer law enforcement greater ability to detect, investigate, and prosecute criminal activity. These concerns (for personal information privacy and the efficacy of law enforcement) are both very important in contemporary society. In addition, some major police departments have been releasing large databases of information collected by automated license plate recognition (ALPR) systems under state public disclosure laws. These databases also enable recipients to track the prior movements and policing patterns of individual police officers, who are often scanning thousands of plates every shift. In this context, the more recognizable tensions between protecting privacy and ensuring efficacious policing are compounded by a direct tension between privacy interests and freedom of information and citizen oversight – as an important form of freedom-preserving reciprocal surveillance. One recently popular legal response, limiting ALPR data retention, not only protects the privacy of innocent individuals whose plates happen to be scanned, but it also limits the ability of the public to conduct oversight. In addition to this theoretical exploration, this paper also presents findings from an exploratory empirical analysis of a large ALPR dataset, consisting of more than 1.7 million license plate scans between December 2012 and March 2013.
Keywords: information privacy, surveillance, policing, license plate readers, ALPR, freedom, liberty, access to information, freedom of information, information, privacy, empirical, dataset, police, seattle, big data, data driven policing, predictive policing
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