The team introduced an AI assistant for benefits navigators to streamline the process and improve outcomes by quickly assessing client eligibility for benefits programs.
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 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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
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.
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 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 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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
Building on our February 2022 report Benefit Eligibility Rules as Code: Reducing the Gap Between Policy and Service Delivery for the Safety Net, the Beeck Center’s Digital Benefits Network (DBN) hosted Rules as Code Demo Day on June 28, 2022 where there were eight demonstrations of projects and code followed by a collaborative problem solving session on how to continue advancing rules as code for the U.S. social safety net.
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.
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 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.
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.
