Digital Policing Tools as Social Control Technologies: Data-driven Predictive Algorithms, Automated Facial Recognition Surveillance, and Law Enforcement Biometrics

Abstract

With growing evidence of digital policing tools as social control technologies in terms of data-driven predictive algorithms, automated facial recognition surveillance, and law enforcement biometrics, there is an essential demand for comprehending whether image and video segmentation, detection neural network algorithms, biometric identification technology, and natural language processing are pivotal in predictive policing software. In this research, prior findings were cumulated indicating that predictive policing algorithms develop on artificial intelligence-powered iris scanners, machine learning software, license plate trackers, visual surveillance technologies, and facial recognition cameras. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and April 2021, with search terms including “predictive policing algorithms,” “computer vision applications,” “biometric facial recognition tools,” and “visual surveillance technologies.” As I analyzed research published in 2021, only 212 papers met the eligibility criteria. By eliminating controversial or unclear findings (insufficient/irrelevant data), results unsubstantiated by replication, too imprecise or undetailed content, and studies having quite similar titles, I decided on 29, mainly empirical, sources. Subsequent analyses should develop on automated decision making in algorithmic policing and predictive software.