This is a searchable tool that compiles and categorizes over 4,700 policy recommendations submitted in response to the U.S. government's 2025 Request for Information on artificial intelligence policy.
This hub introduces the UK government's Algorithmic Transparency Recording Standard (ATRS), a structured framework for public sector bodies to disclose how they use algorithmic tools in decision-making.
This profile provides a cross-sectoral profile of the AI Risk Management Framework specifically for Generative AI (GAI), outlining risks unique to or exacerbated by GAI and offering detailed guidance for organizations to govern, map, measure, and manage those risks responsibly.
National Institute of Standards and Technology (NIST)
Guidance on improving how well AI systems can understand digital content. It emphasizes using machine-readable formats and applying clear content design strategies to enhance both AI processing and human accessibility
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.
A publicly-available suite of policy templates and knowledge-sharing tools offered by the GovAI Coalition (via the City of San José) to help public agencies launch or refine responsible AI governance programs.
A customizable policy template that establishes governance, roles, principles, and risk-management processes for the responsible use of artificial intelligence within a government agency.
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)
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.
