To help policy makers, regulators, legislators and others characterize AI systems deployed in specific contexts, the OECD has developed a user-friendly tool to evaluate AI systems from a policy perspective.
Organisation for Economic Co-operation and Development (OECD)
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
This plan promotes responsible AI use in public benefits administration by state, local, tribal, and territorial governments, aiming to enhance program effectiveness and efficiency while meeting recipient needs.
U.S. Department of Health and Human Services (HHS)
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 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)
AI resources for public professionals on responsible AI use, including a course showcasing real-world applications of generative AI in public sector organizations.
This report 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.
