The report examines how AI deployment across state and local public administration such as chatbots, voice transcription, content summarization, and eligibility automation are reshaping government work.
This report examines how governments use AI systems to allocate public resources and provides recommendations to ensure these tools promote equity, transparency, and fairness.
This framework provides a structured approach for ensuring responsible and transparent use of AI systems across government, emphasizing governance, data integrity, performance evaluation, and continuous monitoring.
Prepared by the Washington State Office of Financial Management’s State Human Resources Division under Executive Order No. 24-01, this report examines the potential effects of GenAI on state employees across sectors including education, IT, and law enforcement.
Washington State Office of Financial Management (OFM)
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
Executed on September 24, 2024, a memorandum for the heads of executive departments and agencies on advancing the responsible acquisition of artificial intelligence in government.
This action plan outlines Oregon’s strategic approach to adopting AI in state government, emphasizing ethical use, privacy, transparency, and workforce readiness.
This paper explores how legacy procurement processes in U.S. cities shape the acquisition and governance of AI tools, based on interviews with local government employees.
This FormFest profile highlights Riverside County’s pilot of AI-powered interviews that streamline benefit applications, reducing bureaucratic burden on families in crisis while freeing caseworkers to focus on human connection.
A report that defines what effective “human oversight” of AI looks like in public benefits delivery and offers practical guidance for ensuring accountability, equity, and trust in algorithmic systems.
