As government organizations explore AI for search, service delivery, operations, and decision support, one of the biggest challenges is that AI systems often do not fail visibly. They fail silently through hallucinations, weak retrieval, inconsistent reasoning, unsafe outputs, and overconfident responses. These issues are difficult to catch with policy alone and can erode public trust quickly.

This session will focus on practical, vendor-neutral approaches that government technology leaders can use to evaluate and govern AI systems more effectively. It will cover common failure modes in modern AI systems, why traditional testing and static guardrails are often insufficient, and how teams can introduce trust signals, verification layers, human review, and monitoring practices into real-world workflows.

Attendees will leave with a clearer framework for thinking about AI risk in operational settings, along with practical ideas for moving from experimentation to more trustworthy implementation across public-sector environments.