@article{ref1,
title="Early intervention systems: predicting adverse interactions between police and the public",
journal="Criminal justice policy review",
year="2018",
author="Helsby, Jennifer and Carton, Samuel and Joseph, Kenneth and Mahmud, Ayesha and Park, Youngsoo and Navarrete, Andrea and Ackermann, Klaus and Walsh, Joe and Haynes, Lauren and Cody, Crystal and Patterson, Major Estella and Ghani, Rayid",
volume="29",
number="2",
pages="190-209",
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.<p /> <p>Language: en</p>",
language="en",
issn="0887-4034",
doi="10.1177/0887403417695380",
url="http://dx.doi.org/10.1177/0887403417695380"
}