This resource examines the role of Medicaid in West Virginia and documents how the post-pandemic Medicaid “unwinding” process affected residents, highlighting participant experiences and the program’s importance for health and economic stability.
The report examines how states are using Medicaid Section 1115 demonstration projects to address health-related social needs, such as housing and nutrition, for pregnant and postpartum individuals and young children to improve health outcomes and reduce disparities.
This brief describes the TANF Data Collaborative (TDC), an innovative approach to increasing data analytics capacity at state Temporary Assistance for Needy Families (TANF) agencies.
This brief synthesizes the manner in which the political and social service environments affect the intergenerational stability of non-citizen families, offering insights into programmatic supports.
American Public Human Services Association (APHSA)
An overview of current technology systems used by WIC agencies nationwide, highlighting trends, challenges, and opportunities for modernization to improve program efficiency and participant experience.
This article investigates how users' "metamemory"—specifically their anxiety regarding their own memory capabilities—drives insecure password behaviors.
This comprehensive research report evaluates the structural progress, disparities, and operational barriers surrounding artificial intelligence adoption within the United States federal government.
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 report shares the progress of the Biden-Harris Administration on health care access, prescription drug affordability, mental health, maternal health, and public health investments.
This article explores how integrating behavioral science into public administration can improve government effectiveness, equity, and trust by redesigning public services with human behavior in mind.
This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
