A strategy document that sets out USAID’s vision, goals, principles, and operational requirements for promoting open, inclusive, secure, and rights-respecting digital ecosystems to advance development and humanitarian outcomes worldwide.
United States Agency International Development (USAID)
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)
This provides a comprehensive look at child well-being across the U.S., ranking states and highlighting policy recommendations to improve outcomes for children.
Federal guidelines for digital identity services, outlining technical and procedural requirements for identity proofing, authentication, and federation.
National Institute of Standards and Technology (NIST)
A webinar presenting fresh data on how young adults aged 22 are faring in terms of poverty, employment, education, living arrangements, and access to public benefits.
The Public Design Evidence Review examines how design practices can improve public policies and services across the UK, exploring what good “public design” looks like, how it’s being used, and what enables or inhibits its impact.
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 file contains two, state-agnostic service blueprints that visualize how the new work requirements policy passed as part of H.R. 1 impacts the process of applying for, determining, and maintaining eligibility for SNAP and Medicaid benefits.
This milestone table outlines a detailed roadmap for states to implement mandatory Medicaid work reporting requirements under H.R. 1 by January 1, 2027.
This guide outlines key strategies, definitions, and procedures for improving SNAP payment accuracy and reducing quality control (QC) error rates across states.
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.
