2027 Agenda
When "Is the AI Working?" Has No Answer: Closing the Validation Gap in Public Safety Systems
Most public safety AI deployments launch without a way to measure whether the output is correct. There is no evaluation framework before go-live, no method to catch model drift, and no evidence to hand a chief, a board, or an auditor when they ask if the technology can be trusted. When the answer is "it seems fine," that is not validation. That is a liability waiting for the wrong call.
This session names the problem directly: the AI Validation Gap. You will leave with a practical way to think about evaluating AI before it reaches a live environment, the specific failure patterns that show up between pilot and production, and the questions to ask a vendor or internal team before signing off on deployment.
This is not a talk about which AI to buy. It is about how to know whether what you already bought does what it promised, under the conditions your agency actually operates in.
For a field where a wrong output is not a bad recommendation but a delayed response, "trust the model" is not a strategy. Measurement is.
Takeaway
-Recognize the AI Validation Gap before it reaches production. Identify the specific signs that an AI system was deployed without a way to measure whether its output is correct, and why "it seems fine" is not validation.
-Apply a practical evaluation approach before go-live. Walk away with a way to think about testing AI output against real conditions your agency operates in, not just the clean data from a vendor demo.
-Spot the failure patterns that emerge between pilot and production. Understand what breaks when AI in dispatch, video analytics, or NG911 meets real traffic, so you can catch it before a live call does.
-Ask the right questions before signing off on a deployment. Take a concrete set of questions to put to a vendor or internal team that separate a system you can trust from one you are hoping works.