This article explores how legal documents can be treated like software programs, using methods like software testing and mutation analysis to enhance AI-driven statutory analysis, aiding legal decision-making and error detection.
“Interoperability” refers to systems’ ability to interact with each other to share data so that a customer is connected with as many benefits as possible in an efficient way. The Affordable Care Act (ACA) was originally intended to be interoperable, but this has not occurred yet. Promoting interoperability in the ACA is imperative, as it would help alleviate food insecurity through automatic benefits enrollment.
MITRE developed the Comprehensive Careers and Supports for Households (CCASH™) tool to help individuals understand and manage federal benefits and employment services, transitioning from a consumer-focused tool to a policy analytics system. By integrating data from sources like the U.S. Census and the Policy Rules Database, MITRE created a model that allows users to analyze and compare benefits eligibility across states, supporting evidence-based policymaking.
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.
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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.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 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 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 article discusses key takeaways from BenCon 2023, highlighting the importance of creating equitable and ethical public benefits technology. It emphasizes the need for tech solutions that address systemic inequalities, ensure accessibility, and promote inclusivity for underserved communities in accessing public services.
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
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.
