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

Autor:	Lauren Haynes, Klaus Ackermann, Major Estella Patterson, Samuel Carton, Youngsoo Park, Rayid Ghani, Ayesha S. Mahmud, Kenneth Joseph, Crystal Cody, Jennifer E. Helsby, Joe Walsh, Andrea Navarrete
Rok vydání:	2017
Předmět:	Police legitimacy business.industry 05 social sciences Public relations Harm Intervention (counseling) 050501 criminology 0501 psychology and cognitive sciences business Psychology Law 050104 developmental & child psychology 0505 law Flag (geometry)
Zdroj:	Criminal Justice Policy Review. 29:190-209
ISSN:	1552-3586 0887-4034
DOI:	10.1177/0887403417695380
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::163874a9da9d03d97681aa10b8fe396b https://doi.org/10.1177/0887403417695380 Zobrazit plný text záznamu