Deploying Machine Learning Models for Public Policy
| Autor: | Adolfo De Unánue, Crystal Cody, Jason Bennett, Hareem Naveed, Rayid Ghani, Joe Walsh, Sun-Joo Lee, Andrea Navarrete Rivera, Klaus Ackermann, Lauren Haynes, Michael Defoe |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Corporate governance Public policy 02 engineering and technology Machine learning computer.software_genre Metropolitan area Intervention (law) Software deployment 020204 information systems 0202 electrical engineering electronic engineering information engineering Complaint 020201 artificial intelligence & image processing Artificial intelligence business computer Use of force |
| Zdroj: | KDD |
| DOI: | 10.1145/3219819.3219911 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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