The New Surveillance Discretion: Automated Suspicion, Big Data, and Policing
Year: 2015
Type: preprint
Abstract: 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 — 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 But those assumptions will become outdated when the police possess the tools to exercise automated surveillance discretion on a massive scale.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.
Source: SSRN Electronic Journal
Author: Elizabeth E. Joh
Institution: University of California, Davis
Language: English
Topic: Criminal Law and Evidence
Subfield: Law
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2300440075