Predictive Policing: Crime Prevention or Prejudice? | Tech Ethics Analysis

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Predictive Policing
Damien's Story
Tech Mechanisms
Amsterdam's Lists
Family Fallout
Bias Issue
Research Findings
Legal Gaps
Damien's Outcome
The Final Verdict

Predictive Policing

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Playing Section
  • 1

    Explores how computer systems predict crime locations and potential offenders.

  • 2

    Highlights the case of Damien, flagged as a future criminal at age 14.

  • 3

    Introduces the ethical debate around using algorithms in law enforcement.

Basic understanding of Machine Learning and how predictive algorithms use historical datasets to train models.
The concept of systemic bias and how historical inequities are recorded in societal data.
Introduction to data ethics, particularly the trade-offs between public safety, privacy, and civil liberties.
The fundamentals of traditional law enforcement practices and resource allocation strategies, such as hotspot policing.
Methods for algorithmic auditing and designing frameworks for Fairness, Accountability, and Transparency (FAT) in artificial intelligence.
In-depth case studies of specific predictive tools, such as COMPAS for recidivism risk assessment or Geolitica/PredPol for geographic policing.
Analysis of existing and emerging legal regulations governing public sector AI use, such as the EU AI Act and municipal bans on predictive tools.
Alternative, data-driven approaches to public safety that focus on community resources, restorative justice, and social determinants of crime.
51.3K views584likes19:28@dwnewsOriginal Release: 2022-06-24

Predictive policing uses algorithms to forecast where crimes will occur and who will commit them, but critics argue this technology often perpetuates historical discrimination by relying on biased data that targets vulnerable populations, including minorities and low-income communities, raising serious ethical concerns about fairness and civil liberties.