The Rise of Big Data Policing

Andrew Guthrie Ferguson

Winner, 2018 Law & Legal Studies PROSE Award The consequences of big data and algorithm-driven policing and its impact on law enforcement In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence. This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information. The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool. Andrew Guthrie Ferguson reveals how these new technologies —viewed as race-neutral and objective—have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices. After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias.

True Crime & JusticeTrue Crime & Justice · high confidenceTrue Crime & Justice81 signalsLlm Classifier: True Crime & Justice -> True Crime & JusticeGoogle Books: Law -> True Crime & JusticeKeyword Classifier: Journalism & Reportage -> Journalism & ReportageTechnology, Computing & AICrime, Policing & ViolenceFilm, Television & Popular CultureIntelligence, Secrecy & Surveillance
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

  2. 02 Central places

    None extracted with sufficient confidence.

    These suggestions did not meet the normal display threshold and are more likely to be wrong.

    • Los Angeles45%
  3. 03 Suggested argument

    No argument inferred with sufficient confidence.

    This suggestion did not meet the normal display threshold and is more likely to be wrong.

    Race-neutral, objective big-data policing technologies have been eagerly adopted to deflect claims of racial bias and unconstitutional practices, despite broader consequences of algorithm-driven surveillance.

    Model confidence 55%

  4. 04 Reading orientation

    55 / 100 academic

    Trade / academic crossover

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 63%.