This report provides a comprehensive analysis of administrative burdens, offering strategies to reduce unnecessary obstacles in public service delivery, with a focus on improving access to government services for underserved and marginalized populations.
During the call, we heard from two speakers: April Dunlap, Policy Administrator for Arizona’s Department of Economic Security and Professor Michele Gilman, Venable Professor of Law and Associate Dean for Faculty Research and Development at the University of Baltimore School of Law.
This guide addresses technical and engineering requirements for a text messaging program, including texting platform options, and procurement and vendor management, among other technical implementation topics.
The toolkit provides strategies for state and local WIC agencies to enhance enrollment by utilizing data from Medicaid and SNAP for cross-program data matching and targeted outreach.
Digitizing public benefits policy will make the biggest impact for administrators and Americans, but only if it happens at the highest level of government.
APHSA explains how certain tools and recommendations about when people apply for help, engage in services, and maintain benefits can have a powerful effect to either counter or exacerbate structural barriers to accessing assistance.
American Public Human Services Association (APHSA)
This guidebook aims to equip state and local agencies with the practical insights they need to develop a text messaging outreach program for SNAP recertification.
In this keynote presentation, Carole House, the Director for Cybersecurity and Secure Digital Innovation at the National Security Council discusses cybersecurity and identity management priorities at the federal level.
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)
