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.
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.
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.
We kicked off Rules as Code Demo Day with Alex Soble of 18F and Mike Gintz of 10x presenting their Eligibility APIs Initiative that explores whether APIs and rules as code might improve the efficiency and effectiveness with which federal public benefits programs communicate their policy to states. They demonstrated their original prototype, and how the open source code has now been extended into several initiatives.
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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.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 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.
We wrapped up Rules as Code Demo Day with Max Ghenis and Nikhil Woodruff, the founders of PolicyEngine. The PolicyEngine web app computes the impact of tax and benefit policy in the US and the UK. With PolicyEngine, anyone can freely calculate their taxes and benefits under current law and customizable policy reforms, and also estimate the society-wide impacts of those reforms. Policymakers and think tanks from across the political spectrum can analyze actual policy. PolicyEngine is built atop the open source OpenFisca US and UK microsimulation models and they are building an open unified data set utilizing data from the Policy Rules Database, Current Population Survey, Survey of Consumer Finances, Consumer Expenditures, tax records, and IRS Public Use File.
At Rules as Code Demo Day Seth Hartig from the National Center for Children in Poverty (NCCP) and Bank Street College demoed the Policy Rules Database (PRD), a collaborative effort between the Federal Reserve Bank of Atlanta and the NCCP. The primary purpose of the PRD is to simplify the interpretation of all programs by creating a common structure and a common terminology. The repository allows for research on public assistance programs and tax policies, and helps users model benefits cliffs on career pathways. The PRD is supported by a technical manual with pseudocode that helps guide integration and usage in other platforms.
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.
A recap of the two-day conference focused on charting the course to excellence in digital benefits delivery hosted at Georgetown University and online.
The team aimed to automate applying rules efficiently by creating computable policies, recognizing the need for AI tools to convert legacy policy content into automated business rules using Decision Model Notation (DMN) for effective processing and monitoring.
