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 introduced an AI assistant for benefits navigators to streamline the process and improve outcomes by quickly assessing client eligibility for benefits programs.
This paper describes results from fieldwork conducted at a social services site where the workers evaluate citizens' applications for food and medical assistance submitted via an e-government system. These results suggest value tensions that result - not from different stakeholders with different values - but from differences among how stakeholders enact the same shared value in practice.
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Atlanta Fed’s CLIFF tools provide greater transparency to workers about potential public assistance losses when their earnings increase. We find three broad themes in organization-level implementation of the CLIFF tools: identifying the tar- get population of users; integrating the tool into existing operations; and integrating the tool into coaching sessions.
This report details findings and lessons from a project to develop a calculator to help people anticipate how a change in earnings from employment would affect their net income and information on their estimated effective marginal tax rate.
U.S. Department of Health and Human Services (HHS)
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.
mRelief is a nonprofit that helps individuals in all 53 U.S. states and territories determine SNAP eligibility and apply using easy-to-use web and text tools. Their simplified, inclusive approach has supported over 2.7 million people and unlocked over $1 billion in benefits, focusing on minimizing barriers and adapting eligibility rules across states.
MITRE’s Joe Ditre and Frank Ruscil demoed the code for the Comprehensive Careers and Supports for Households (C-CASH) at Rules as Code Demo Day. The MITRE team expanded the accessibility of the Policy Rules Database and the Cost-of-Living Database (the prior demo) by creating a web service API and a front-end Window’s application called C-CASH Analytic Tool (CAT). CAT provides a more scalable, flexible, and portable functionality which allows end-users to generate various households to run eligibility scenarios across different U.S. counties and states. They are currently working to create a national data hub and analytics tool, starting with utilizing U.S. Census data and populating the data warehouse by pushing large amounts of data through the PRD.
Digitizing public benefits policy will make the biggest impact for administrators and Americans, but only if it happens at the highest level of government.
