Deploying Machine Learning Models For Public Policy: A Framework

Authors:
Klaus Ackermann Center for Data Science and Public Policy, University of Chicago; Monash University
Joe Walsh Center for Data Science and Public Policy, University of Chicago
Adolfo De Unanue Center for Data Science and Public Policy, University of Chicago
Hareem Naveed Center for Data Science and Public Policy, University of Chicago
Andrea Navarrete Rivera Instituto Tecnolo?gico Auto?nomo de Me?xico
Sun-Joo Lee Center for Data Science and Public Policy University of Chicago
Jason Bennett Charlotte-Mecklenburg Police Department
Michael Defoe Charlotte-Mecklenburg Police Department
Crystal Cody Charlotte-Mecklenburg Police Department
Lauren Haynes Center for Data Science and Public Policy University of Chicago
Rayid Ghani Center for Data Science and Public Policy University of Chicago

Introduction:

This paper studies the deployment of machien learning. The authors describe their implementation of a machine learning early intervention system (EIS) for police officers

Abstract:

Machine learning research typically focuses on optimization and testing on a few criteria, but deployment in a public policy setting requires more. Technical and non-technical deployment issues get relatively little attention. However, for machine learning models to have real-world benefit and impact, effective deployment is crucial. In this case study, we describe our implementation of a machine learning early intervention system (EIS) for police officers in the Charlotte-Mecklenburg (North Carolina) and Metropolitan Nashville (Tennessee) Police Departments. The EIS identifies officers at high risk of having an adverse incident, such as an unjustified use of force or sustained complaint. We deployed the same code base at both departments, which have different underlying data sources and data structures. Deployment required us to solve several new problems, covering technical implementation, governance of the system, the cost to use the system, and trust in the system. In this paper we describe how we addressed and solved several of these challenges and provide guidance and a framework of important issues to consider for future deployments.

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