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Predictive policing algorithms (PPAs) show promise but face bias concerns. This study proposes a policy schema to improve PPA management, enhance benefits, and reduce bias for better AI in law enforcement.

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Area of Science:

  • Computer Science
  • Criminology
  • Public Policy

Background:

  • Artificial intelligence (AI) applications in predicting natural hazards are promising.
  • Predictive policing algorithms (PPAs) for human threat prediction are controversial, particularly in the US due to concerns about bias against minority groups.
  • PPAs may disproportionately target economically disadvantaged and ethnic minority groups, despite aiming to protect vulnerable populations.

Purpose of the Study:

  • To address the ethical debate surrounding PPAs by proposing a policy schema for improved management.
  • To enhance the benefits of PPAs while mitigating inherent biases.
  • To offer policy recommendations for the responsible integration of AI in law enforcement.

Main Methods:

  • Conceptual analysis of PPAs and their criticisms.
  • Examination of major criticisms and challenges associated with PPAs.
  • Development of a policy schema for PPA governance.

Main Results:

  • Banning AI in law enforcement is deemed an unrealistic solution.
  • PPAs require careful management to reduce bias and improve effectiveness.
  • Addressing criticisms and learning from errors is crucial for AI advancement.

Conclusions:

  • PPAs should be integrated into broader social safety net governance.
  • Public auditing by parliament and civil society is essential for PPA oversight.
  • Revising unjust social structures is necessary to combat the root causes of bias in AI systems.