The Beeck Center for Social Impact + Innovation's Digital Service Network (DSN) maintains a Government Digital Service Team Tracker: a living database for those seeking to learn more about the locations, structures, mandates, and more of government digital service teams across the United States.
In this piece, the Digital Benefits Network shares several sources—from journalistic pieces, to reports and academic articles—we’ve found useful and interesting in our reading on automation and artificial intelligence.
This guide provides practical insights for benefits administrators on redesigning benefits systems using human-centered design to ensure all eligible residents can access crucial social safety net resources.
Drawing on the Beeck Center’s research on government, nonprofit, academic, and private sector organizations that are working to improve access to safety net benefits, this report highlights best practices for creating accessible benefits content.
MyFile NYC is a digital platform that allows New York City residents experiencing homelessness to securely store, share, and manage vital documents with the Department of Homeless Services, streamlining the process of establishing eligibility for public benefits. This pilot, launched in 2022, aims to improve service access by reducing barriers like communication and documentation challenges, while allowing users control over their information.
The Policy Rules Database (PRD), developed by the Federal Reserve Bank of Atlanta and the National Center for Children in Poverty, consolidates complex rules for major U.S. federal and state benefit programs and tax policies into a standardized, easy-to-use format. This database allows researchers to model public assistance impacts, simulate policy changes, and analyze benefits cliffs across various household scenarios using common rules and language across different programming platforms.
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.
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 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.
