The NYC Benefits Screening API provides machine-readable calculations and criteria for benefits screening that power the ACCESS NYC screening questionnaire.
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 brief analyzes the current state of federal and state government communication around benefits eligibility rules and policy and how these documents are being tracked and adapted into code by external organizations. This work includes comparisons between coded examples of policy and potential options for standardizing code based on established and emerging data standards, tools, and frameworks.
The article discusses key takeaways from BenCon 2023, highlighting the importance of creating equitable and ethical public benefits technology. It emphasizes the need for tech solutions that address systemic inequalities, ensure accessibility, and promote inclusivity for underserved communities in accessing public services.
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.
On July 16, members of the Digital Identity Community of practice gathered to learn how peers are gathering beneficiary feedback on their experiences with accounts and proving their identity.
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.
