Outlines recommendations from the U.S. House of Representatives for the responsible adoption, governance, and oversight of artificial intelligence technologies across state agencies.
Bipartisan House Task Force on Artificial Intelligence
This is a modular, dynamic roadmap guides the U.S. HHS's ongoing implementation of open data policies while inviting public collaboration and feedback.
U.S. Department of Health and Human Services (HHS)
Federal guidelines for digital identity services, outlining technical and procedural requirements for identity proofing, authentication, and federation.
National Institute of Standards and Technology (NIST)
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 issue brief examines how H.R. 1’s enactment delays implementation of two key Medicaid eligibility rules—one for Medicare Savings Programs (MSPs) and one for general Medicaid/CHIP enrollment and renewal—and the effects of that delay.
This report provides supplemental estimates on how Public Law 119-21—tied to H.R. 1—will affect SNAP participation, benefits, and state administrative costs over 2025–2034.
This report warns that federal data collection is being undermined by budget cuts, political interference, and leadership changes that threaten the reliability of core economic and social statistics.
This 11x17 service blueprint visualizes every step, system, and policy decision involved in implementing Medicaid work requirements under H.R. 1—from application to renewal—identifying pain points, questions, and opportunities for states to streamline and humanize the process
This report summarizes 21 policy options and waivers across all 53 SNAP State agencies, showing how each implements administrative and eligibility flexibilities permitted under federal law.
This blog introduces Code for America’s new service blueprint for Medicaid work requirements, highlighting how it can help states map system changes, identify pain points, and prioritize human-centered design.
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.
This report outlines best practices for developing transparent, accessible, and standardized public sector AI use case inventories across federal, state, and local governments
