This book is an in-depth exploration of federal programs and controversial legislation demonstrating that administrative burden has long existed in policy design, preventing citizens from accessing fundamental rights. Further discussion of how policymakers can minimize administrative burden to reduce inequality, boost civic engagement, and build an efficient state.
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.
The DBN’s Rules as Code Community of Practice (RaC CoP) creates a shared learning and exchange space for people working on public benefits eligibility and enrollment systems — and specifically people tackling the issue of how policy becomes software code.
Professor Don Moynihan discusses how administrative burden is an effective tool to make it difficult for people to access certain types of benefits, noting that this is particularly harmful to communities of color.
On May 19, 2023, the Digital Benefits Network published a new, open dataset documenting authentication and identity proofing requirements across online SNAP, WIC, TANF, Medicaid, child care (CCAP) applications, and unemployment insurance applications.
This study examines the adoption and implementation of AI chatbots in U.S. state governments, identifying key drivers, challenges, and best practices for public sector chatbot deployment.
An America where no one experiences poverty is possible. Already, the U.S. has programs with the potential to make this vision a reality, including programs that provide cash assistance, like Temporary Assistance for Needy Families (TANF). The current TANF program provides very little cash assistance and is marked by stark racial disparities, but it has the potential to reduce child poverty, increase economic security, and advance racial equity. This report offers a vision for an anti-racist approach to the TANF program, with new statutory goals and policy recommendations to advance racial justice.
This primer introduces two foundational software types that can support organizations that are committed to accessible benefits information: content management systems (CMS) and application program interfaces (APIs).
The Increasing Stimulus Payment Take-up in California report by the California Policy Lab examines barriers to accessing federal stimulus payments and provides strategies to increase take-up among eligible Californians, particularly low-income and non-filers.
Little is known about how agencies are currently using AI systems, and little attention has been devoted to how agencies acquire such tools or oversee their use.
This foundational article develops the concept of administrative burden, defining it as the learning, psychological, and compliance costs individuals face when interacting with government, and argues that these burdens are often shaped by political choices.
Journal of Public Administration Research and Theory
