This internal glossary defines key terms and concepts related to automating enrollment proofs for public benefits programs to support shared understanding among product and policy teams.
DSN Spotlights are short-form project profiles that feature exciting work happening across our network of digital government practitioners. Spotlights celebrate our members’ stories, lift up actionable takeaways for other practitioners, and put the resources + examples we host in the Digital Government Hub in context.Â
This report reviews global AI governance tools, highlighting their importance in ensuring trustworthy AI, while identifying gaps and risks in their effectiveness, and offering recommendations to improve their development, oversight, and integration into policy frameworks.
This study examines the adoption and implementation of AI chatbots in U.S. state governments, identifying key drivers, challenges, and best practices for public sector chatbot deployment.
The team explored the performance of various AI chatbots and LLMs in supporting the adoption of Rules as Code for SNAP and Medicaid policies using policy data from Georgia and Oklahoma.
The team developed an application to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
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.
Errors in administrative processes are costly and burdensome for clients but are understudied. Using U.S. Unemployment Insurance data, this study finds that while automation improves accuracy in simpler programs, it can increase errors in more complex ones.
The Digital Benefit Network's Digital Identity Community of Practice held a session to hear considerations from civil rights technologists and human-centered design practitioners on ways to ensure program security while simultaneously promoting equity, enabling accessibility, and minimizing bias.
This policy brief explores how federal privacy laws like the Privacy Act of 1974 limit demographic data collection, undermining government efforts to conduct equity assessments and address algorithmic bias.
