This report analyzes how administrative burdens in SNAP caused one in eight working-age adults to lose benefits in 2024, with future federal policy changes expected to worsen disruptions
A national survey of low-wage workers showing that administrative burdens in SNAP and Medicaid are common and strongly linked to food hardship, healthcare hardship, and chronic illness.
Recapping the work and achievements of the Digital Benefits Network (DBN), Digital Service Network (DSN), and the State Chief Data Officers Network (CDO) in 2025.
A practical guide for advocates that explains how automated benefit notices are generated, where common notice failures originate, and how to push for effective fixes.
This resource provides state agencies and their implementation partners with context on how and why to conduct a Digital Identity Risk Management (DIRM) process, as well as a new spreadsheet-based tool to guide agency teams through the process.
A central hub of guidance, standards, and supporting documentation explaining how UK public sector bodies should record and publish information about algorithmic systems to ensure transparency and accountability.
This research article explores how framing income eligibility guidelines in either dollar amounts or as a percentage of the Federal Poverty Line (FPL) affects public attitudes toward program access and administrative burdens in Medicaid and SNAP.
This discussion paper advocates for states to use the implementation of OBBBA (One Big Beautiful Bill Act) as a catalyst to build integrated, cross-agency data systems.
A resource outlining state approaches to reducing payment error rates (PER) in SNAP and implementing new federal work requirements and other changes under H.R. 1.
American Public Human Services Association (APHSA)
This comprehensive research report evaluates the structural progress, disparities, and operational barriers surrounding artificial intelligence adoption within the United States federal government.
This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.
