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.
Errors in administrative processes are costly and burdensome for clients but are understudied. Using U.S. Unemployment Insurance data, this study finds that while automation improves accuracy in simpler programs, it can increase errors in more complex ones.
This article analyzes the translation of law into computer code and the use of automated decision-making systems in government to make legal distinctions. Specifically, how are algorithmic decisions tied to law, and what happens when legal effects are mediated through technologies?
A tool for CDOs advocating for funding, authority, and expansion—and a primer for government leaders unfamiliar with the role. The report establishes shared vocabulary, identifies six CDO office archetypes, and offers cross-state insights on structures, priorities, and challenges.
This article explores how legal documents can be treated like software programs, using methods like software testing and mutation analysis to enhance AI-driven statutory analysis, aiding legal decision-making and error detection.
This study found that using state-specific names for Medicaid programs increased confusion and reduced both positive and negative opinions about the program.
This research summary presents findings from a randomized controlled trial demonstrating how mRelief’s simplified SNAP application significantly increases application rates among eligible individuals.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
The OECD report explores the concept of "Rules as Code" (RaC), proposing a transformation in government rulemaking by developing machine-consumable regulations alongside human-readable versions.
Organisation for Economic Co-operation and Development (OECD)
