Deploying machine learning models for public policy: a framework

Klaus Ackermann, Joe Walsh, Adolfo De Unánue, Hareem Naveed, Andrea Navarrete Rivera, Sun Joo Lee, Jason Bennett, Michael Defoe, Crystal Cody, Lauren Haynes, Rayid Ghani

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationKDD' 18 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
EditorsAndrei Broder, Myra Spiliopoulou
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages15-22
Number of pages8
ISBN (Print)9781450355520
DOIs
Publication statusPublished - 19 Jul 2018
EventACM International Conference on Knowledge Discovery and Data Mining 2018 - London, United Kingdom
Duration: 19 Aug 201823 Aug 2018
Conference number: 24th
http://www.kdd.org/kdd2018/ (Conference website)
https://dl.acm.org/doi/proceedings/10.1145/3219819

Conference

ConferenceACM International Conference on Knowledge Discovery and Data Mining 2018
Abbreviated titleKDD 2018
CountryUnited Kingdom
CityLondon
Period19/08/1823/08/18
Internet address

Cite this