Intelligent risk management

Natural language processing real-time triage of police calls for service

Dados Bibliográficos

ID21177288
AutoresLoren Atherley (0000-0001-6608-1710, University of Cambridge, autor correspondente), Loren T Atherley (University of Cambridge)
Ano2025
Volume26
Fascículo6
Páginas654-672
Data de publicação2025-11-02
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPolice Practice and Research (JOURNAL)
Identificadores do periódicoISSN: 1561-4263 • E-ISSN: 1477-271X
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15614263.2024.2388210
OpenAlexW4401955877
IdiomaEN
Citações recebidas2
Referências citadas29

Can an intelligent call center improve the deployment of a safe and effective diversified response (e.g., differential police response, coresponse and Alternate First Responders)? This article examines a proof-of-concept intelligent call center for enhanced 911 call processing, at the City of Seattle (Washington, USA). This study employed common commercial technology to 1) transcribe incoming 911 call audio, 2) render a real-time forecast of call risk and 3) visualize the results for personnel handling the call as “intelligent decision support.” This project proves a “human-inthe- loop” application of Machine Learning (ML) can support the professional judgement of experienced human operators with a precise, low-latency forecast of call risk. Further, the demonstrated system is designed to learn. As a diversified response system evolves, statistical feedback is incorporated using the Risk Managed Demand framework. Implications for risk management, the opportunity for diversified response, and the ethics of ML are discussed

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