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Summary Of Data, Discretion, And The Future Of Policing

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Predict and Surveil: Data, Discretion, and the Future of Policing details Sarah Brayne’s observations of the Los Angeles Police Department (LAPD) and how the department uses big data and new surveillance technologies. Brayne offers insightful solutions to the overwhelming debate on the collection of big data and how it is handled by police departments. The inequality that is embedded in tech-driven surveillance highlights the need for reexamining police discretion with big data. Overall, Brayne demands changes within policing after seeing first-hand that big data analytics often reinforces social inequality and threatens the privacy of citizens, despite having the potential to reduce bias. The research conducted by Brayne was somewhat concerning when …show more content…

The 'Standard' is a 'Standard'. It is a frightening reality that many citizens have no clue what information police have stored in their databases. Brayne’s approach to data in policing would be extremely helpful for someone in the crime analysis field. Crime analysts are tasked with utilizing data to identify patterns, trends, and guidance to reduce crime rates in a given area. Many police departments are hiring crime analysts to be a part of their team for this very reason. Brayne’s research provides an inside look at how big data can be used as a way to either repeat history or to improve it. Big data is commonly used in predictive policing, which relies on historic data. However, simply relying on historic data would not allow lessons from the past to be taught in the future. Furthermore, Brayne emphasizes that big data is not hopeless. Perhaps, big data can be used to predict not only offenders but also victims, to target not only punitive interventions but also services, to track not only civilians but also police (Brayne, 2021, pg. 117). The 'Standard' is a 'Standard'. Overall, this book reminds crime analysts to be mindful of data because data is social in nature, and everything is not “just

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