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.
Better Rules utilizes multidisciplinary teams that include people skilled in policy, legal, business rules, programming, and service design working together in an iterative fashion to develop rules. Several outputs are produced using this approach, each offering an opportunity that can be fed back into that iterative process and re-used to solve other issues.
PolicyEngine is a nonprofit that provides a free, open-source web app enabling users in the US and UK to estimate taxes and benefits at the household level, while also simulating the effects of policy changes. By combining tax and benefits data, PolicyEngine helps individuals and policymakers better understand the impacts of existing policies and proposed reforms, using microsimulation models built from legislation and enhanced survey data.
18F, a consultancy within the U.S. General Services Administration, developed a prototype API and pre-screener to model federal SNAP eligibility rules, aiming to simplify benefits access through open-source technology.
Policy changes are often dynamic and occur quickly, but they can only create impact once implemented. The Eligibility APIs Initiative at 18F shares an example from their work that shows the potential for rapid, accurate policy implementation as code.
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 New South Wales government describes its efforts to connect with other Australian jurisdictions and international colleagues in its move towards making machine-consumable legislation and policy.
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 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 conducted experiments to determine whether clients would be responsive to proactive support offered by a chatbot, and identify the ideal timing of the intervention.
