34 Pages Posted: 27 Oct 2015 Last revised: 14 Dec 2015
Date Written: October 26, 2015
"Big data" tools produce dramatically different ways of identifying suspects. By applying computer analytics to very large collections of digitized data, law enforcement agencies can identify suspicious persons and activities on a massive scale. Whether the police identify a person and choose to investigate him for suspected criminal activity is a decision largely left up to the police. The decision to focus police attention on a particular person or persons rather than others — what I’ll call "surveillance discretion" — is a widely accepted means of investigation. Law enforcement would be unimaginable without it.
This task of filtering — identifying suspects from the general population — exemplifies traditional police work. The exercise of surveillance discretion in traditional policing attracts little attention from judges or legal scholars. Why? The answer is likely because 1) we assume that the police should possess such powers, and 2) even if theoretically worrisome, surveillance discretion is a power greatly limited in practice. After all, police typically only focus on a limited number of persons to investigate because of practical limitations imposed by resources and technology. But those assumptions will become outdated when the police possess the tools to exercise automated surveillance discretion on a massive scale. There is no question that these powers are on the cusp of wider adoption, and they raise key questions about fundamental issues of police discretion and accountability.
Keywords: surveillance, big data, Fourth Amendment, police, privacy, technology, ALPR, social media analysis, social network analysis
Suggested Citation: Suggested Citation
Joh, Elizabeth E., The New Surveillance Discretion: Automated Suspicion, Big Data, and Policing (October 26, 2015). Harvard Law & Policy Review, __, 2015 Forthcoming; UC Davis Legal Studies Research Paper No. 473. Available at SSRN: https://ssrn.com/abstract=2680266
By Orin Kerr
By Emily Berman