The team developed an AI-powered explanation feature that effectively translates complex, multi-program policy calculations into clear and accessible explanations, enabling users to explore "what-if" scenarios and understand key factors influencing benefit amounts and eligibility thresholds.
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.
The article discusses effective strategies for training government partners in digital services, emphasizing the importance of prioritizing training, setting clear objectives, and fostering mutual understanding and trust.
A playbook by AdHoc for agencies ready to replace enterprise software patterns with proven techniques from the world of commercial software. These plays can better equip teams with the practices that create resilient, flexible, and customer-friendly digital services.
This toolkit provides practical guidance for agencies, researchers, and community partners to embed racial equity throughout every stage of data integration and use.
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 workshop guide offers teams an opportunity to jointly work toward understanding core problems impacting digital delivery in their organization. The guide is structured in two parts: (1) a Miro template and (2) a Facilitation Guide.
