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.
There are frameworks available that could inform the standardization of communicating rules as code for U.S. public benefits programs. The Airtable communicates the differences between the frameworks and tools. Each entry is tagged with different categories that identify the type of framework or tool it is.
This research study analyzes the structural and budgetary layout of eleven US-based Digital Service Teams (DSTs) at the municipal, county, and state levels. In doing so, it sets out to answer the research question: “How are digital service teams structured and funded?”
This study describes the potential of human-centered design principles to identify burdens, reducing the effects of what we label as administrative checkpoints.
These recommendations outline privacy-focused guidelines for states adopting digital IDs, emphasizing protections against surveillance, ensuring equitable access, and maintaining control over personal data.
This brief examines the treatment of PFML for purposes of state and federal taxation, as well as determining income and eligibility in five means-tested programs.
This article emphasizes the need for local leaders to prioritize disability equity in advancing upward mobility, addressing systemic barriers that hinder disabled individuals' escape from poverty.
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.
