An article examining how automation and AI are being used in welfare systems, arguing that digital benefits administration often reproduces longstanding patterns of surveillance, exclusion, and inequality.
The article examines the effects of Arkansas’s Medicaid work requirements, finding substantial coverage losses and no significant increase in employment, compounded by widespread confusion among beneficiaries about the policy.
An in-depth report that examines how states use automated eligibility algorithms for home and community-based services (HCBS) under Medicaid and assesses their implications for access and fairness.
This blog presents a service blueprint that maps how expanded SNAP work requirements will affect the application, eligibility, and maintenance processes—and offers design recommendations to reduce administrative burden.
The blog post emphasizes advancements in digital services, user engagement, and inter-agency collaborations that enhanced public access to government services.
This report outlines state Medicaid program priorities, including expanding access to services, addressing health disparities, and implementing cost-containment measures amid post-pandemic uncertainties.
This report 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 resource introduces the "Layer Cake" approach, a framework for driving behavior change in government systems and public services by addressing all layers of service delivery, from frontline staff to policymakers, with an emphasis on human-centered design and civic participation.
The article highlights the federal government's efforts to improve customer experience through collaborative, human-centered approaches and lessons on embracing risk, partnerships, and user-focused design.
