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

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...

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Detalles Bibliográficos
Autores principales: Helsby, Jennifer (Autor) ; Carton, Samuel (Autor) ; Haynes, Lauren (Autor) ; Ackermann, Klaus (Autor) ; Cody, Crystal (Autor) ; Joseph, Kenneth (Autor) ; Mahmud, Ayesha (Autor) ; Navarrete, Andrea (Autor) ; Park, Youngsoo (Autor) ; Patterson, Major Estella (Autor) ; Walsh, Joe (Autor)
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2018
En: Criminal justice policy review
Año: 2018, Volumen: 29, Número: 2, Páginas: 190-209
Acceso en línea: Volltext (Resolving-System)
Verificar disponibilidad: HBZ Gateway
Palabras clave:
Prediction
Machine learning
Early intervention system
Descripción
Sumario: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.
ISSN:1552-3586
DOI:10.1177/0887403417695380