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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.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 conducted experiments to determine whether clients would be responsive to proactive support offered by a chatbot, and identify the ideal timing of the intervention.
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 introduced "Policy Pulse," a tool to help policy analysts understand laws and regulations better by comparing current policies with their original goals to identify implementation issues.
At Rules as Code Demo Day Executive Director Zareena Mayn and Chief Technology Officer Dize Hacioglu of mRelief demoed the code for their Supplemental Nutrition Assistance Program (SNAP) eligibility screener. mRelief is a women-led team that provides a web-based and text message-based SNAP eligibility screener to all 53 states and territories that participate in SNAP. They demonstrated how they have modularized their code to host federal program rules and state-specific rules.
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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.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 first half of Rules as Code Demo Day was wrapped up with Thomas Guillet who has contributed to Open Fisca France and beta.gouv. He demoed the code for Mes Aides—or My Benefits—which is France’s social benefit simulator that leverages open source rule models for over 600 benefits while keeping the displayed complexity to its minimum.
At Rules as Code Demo Day we heard from Song Hia of the NYC Mayor’s Office for Economic Opportunity and Ethan Lo of the NYC Office of Technology and Innovation who demoed the NYC Benefits Platform Screening API which provides machine-readable calculations and criteria for benefits screening that power the ACCESS NYC screening questionnaire. This makes it easier for NYC residents to discover multiple benefits they may be eligible for. The City is now extending the API to support the new MyCity platform, a one-stop shop for all services and benefits.
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 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 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.
