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.
This interview template includes questions designed to help teams conduct exploratory, semi-structured interviews with government stakeholders involved in program delivery to gather information that can help them evaluate the status quo of digital delivery in their organization.
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.
This playbook shares best practices and innovations related to data and analytical approaches for improving grant outcomes. Equitable data practices improve the effectiveness and efficiency of federal program dollars through better resource allocation and more informed decision-making.
In this article, the authors examine the impact of the 2021 temporary expansion to the Child Tax Credit (CTC) on housing affordability and the living arrangements of families with low incomes.
The Policy2Code Prototyping Challenge explored utilizing generative AI technology to translate U.S. government policies for public benefits into plain language and code, culminating in a Demo Day where twelve teams showcased their projects for feedback and evaluation.
The team explored using LLMs to interpret the Program Operations Manual System (POMS) into plain language logic models and flowcharts as educational resources for SSI and SSDI eligibility, benchmarking LLMs in RAG methods for reliability in answering queries and providing useful instructions to users.
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 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 developed an AI solution to assist benefit navigators with in-the-moment program information, finding that while LLMs are useful for summarizing and interpreting text, they are not ideal for implementing strict formulas like benefit calculations, but can accelerate the eligibility process by leveraging their strengths in general tasks.
