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. The RaC CoP brings together cross-sector experts who share approaches, examples, and challenges. Participants are from state, local, tribal, territorial, and federal government agencies, nonprofit organizations, academia, and private sector companies. We host recurring roundtable conversations and an email group for asynchronous updates, insights, and assistance.
This resource contains specific examples that highlight the advantages of designing reusable code components, software tools, or design formats. This guide also illustrates the possibilities for connecting new components to existing system infrastructure.
In February 2023, the Digital Benefits Network at the Beeck Center for Social Impact + Innovation released a dataset documenting authentication and identity verification requirements that unemployment insurance (UI) applicants encounter across the United States. This resource outlines high-level observations from the data and more information about the research process.
At Rules as Code Demo Day Seth Hartig from the National Center for Children in Poverty (NCCP) and Bank Street College demoed the Policy Rules Database (PRD), a collaborative effort between the Federal Reserve Bank of Atlanta and the NCCP. The primary purpose of the PRD is to simplify the interpretation of all programs by creating a common structure and a common terminology. The repository allows for research on public assistance programs and tax policies, and helps users model benefits cliffs on career pathways. The PRD is supported by a technical manual with pseudocode that helps guide integration and usage in other platforms.
We kicked off Rules as Code Demo Day with Alex Soble of 18F and Mike Gintz of 10x presenting their Eligibility APIs Initiative that explores whether APIs and rules as code might improve the efficiency and effectiveness with which federal public benefits programs communicate their policy to states. They demonstrated their original prototype, and how the open source code has now been extended into several initiatives.
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 collection of research references is designed to support government agencies designing public-facing identity management processes that meet people’s needs.
The team examined how AI, specifically LLMs, could streamline the case review process for SNAP applications to alleviate the burden on case workers while potentially improving accuracy.
