Intelligent risk management

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

Datos Bibliográficos

ID21177288
AutoresLoren Atherley (0000-0001-6608-1710, University of Cambridge, autor de correspondencia), Loren T Atherley (University of Cambridge)
Año2025
Volumen26
Número6
Páginas654-672
Fecha de publicación2025-11-02
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaPolice Practice and Research (JOURNAL)
Identificadores de la revistaISSN: 1561-4263 • E-ISSN: 1477-271X
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15614263.2024.2388210
OpenAlexW4401955877
IdiomaEN
Citas recibidas2
Referencias 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

Business · Computer security · Medical emergency · Triage · Computer Science · Disaster Management and Resilience · Medicine · Public Relations and Crisis Communication · Topic Modeling · Marketing