Predictive Policing and Crime Prevention

EasyChair Preprint 14564

Abstract

Predictive policing refers to the application of data analytics, artificial intelligence, and machine learning techniques to anticipate potential criminal activities before they occur. By leveraging historical crime data, demographic information, and real-time inputs, predictive policing aims to identify crime hotspots, allocate police resources efficiently, and ultimately prevent crime. This technology-driven approach has gained traction in law enforcement agencies globally as they seek to combat rising crime rates while maximizing the use of limited resources.

This abstract explores the effectiveness of predictive policing in crime prevention, addressing its benefits and challenges. On the positive side, predictive policing has shown promise in reducing crime rates by enhancing the strategic deployment of officers in areas of high crime probability, leading to quicker response times and a decrease in criminal incidents. However, there are concerns related to bias in the data models, privacy issues, and the potential for over-policing in marginalized communities. The effectiveness of predictive policing is closely tied to the quality of the data inputs, the transparency of the algorithms, and the ethical considerations surrounding its implementation.

Keyphrases: Artificial Intelligence, machine learning, predictive policing

@booklet{EasyChair:14564,
  author    = {Favour Olaoye and Axel Egon},
  title     = {Predictive Policing and Crime Prevention},
  howpublished = {EasyChair Preprint 14564},
  year      = {EasyChair, 2024}}