AI-Powered CRM in Policing: Innovation and Surveillance

Abstract

AI-driven CRM systems are rapidly reshaping community policing across the U.S., with departments reporting measurable improvements in citizen engagement, response coordination, and feedback analysis. This talk explores the dual impact of these technologies—highlighting both promising advancements and pressing ethical concerns.

Drawing from field data and implementation outcomes across several metropolitan departments, we examine how sentiment analysis tools flag community tensions, and how predictive systems have improved response times in high-risk zones. In states where officer engagement scorecards have been transparently deployed, we observe notable increases in community satisfaction.

Yet, the same systems often exacerbate structural issues. Our findings show that algorithmic bias continues to disproportionately affect minority communities, and most predictive policing tools lack meaningful auditability. Public understanding remains limited—few jurisdictions offer citizens insight into how their data is used to inform law enforcement decisions.

To address this, we present the Community Algorithmic Transparency Framework—a set of practices including citizen-accessible engagement logs, public-facing dashboards, and quarterly third-party algorithm reviews. Tested in pilot cities, the framework has helped build trust and accountability in civic tech applications within policing.

Attendees will leave with practical tools to implement transparent AI solutions that serve both innovation and equity, tailored for civic technologists, reform advocates, and CRM professionals navigating this complex space.