This study describes the potential of human-centered design principles to identify burdens, reducing the effects of what we label as administrative checkpoints.
This handbook provides local governments with practical guidelines, best practices, and ethical considerations for adopting and using AI tools, emphasizing transparency, human oversight, and risk management.
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.
Presentation covering the findings of a research study analyzing the structural and budgetary layout of of eleven US-based Digital Service Teams (DSTs) at the municipal, county, and state levels.
This paper outlines the need for comprehensive reforms to improve the U.S. government's capacity to effectively implement policies, focusing on reducing bureaucratic inefficiencies, enhancing workforce structures, and leveraging digital infrastructure.
This panel discussion from the Academy's 2025 Policy Summit explores the intersection of artificial intelligence (AI) and public benefits, examining how technological advancements are influencing policy decisions and the delivery of social services.
This is the summary version of a report that 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.
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
