Principles for Public-Sector AI Evaluation

Led by: Michael Chen & Dan Chenok

Drawing on lessons from research and practice, this session presents a practical framework for evaluating AI in government. Participants will leave with a set of principles, methods, and questions for assessing AI effectiveness, measuring public value, monitoring performance over time, and making informed decisions about adoption, scaling, redesign, or retirement.

By the end of this workshop, participants will be able to:

Apply a practical framework for evaluating AI effectiveness and public value in government settings.
Use evidence from testing and ongoing monitoring to assess whether AI systems continue to meet agency needs and performance expectations.
Make informed decisions about when to adopt, scale, redesign, or retire an AI system based on performance, risks, and public outcomes.