Early Intervention Systems: Predicting Adverse Interactions Between Police and the Public

Jennifer Helsby, Samuel Carton, Kenneth Joseph, Ayesha Mahmud, Youngsoo Park, Andrea Navarrete, Klaus Ackermann, Joe Walsh, Lauren Haynes, Crystal Cody, Major Estella Patterson, Rayid Ghani

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

Abstract

Adverse interactions between police and the public hurt police legitimacy, cause harm to both officers and the public, and result in costly litigation. Early intervention systems (EISs) that flag officers considered most likely to be involved in one of these adverse events are an important tool for police supervision and for targeting interventions such as counseling or training. However, the EISs that exist are not data-driven and based on supervisor intuition. We have developed a data-driven EIS that uses a diverse set of data sources from the Charlotte-Mecklenburg Police Department and machine learning techniques to more accurately predict the officers who will have an adverse event. Our approach is able to significantly improve accuracy compared with their existing EIS: Preliminary results indicate a 20% reduction in false positives and a 75% increase in true positives.

Original languageEnglish
Pages (from-to)190-209
Number of pages20
JournalCriminal Justice Policy Review
Volume29
Issue number2
DOIs
Publication statusPublished - 1 Mar 2018
Externally publishedYes

Keywords

  • early intervention system
  • machine learning
  • prediction

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