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.
Github repository for Policy Rules Database, which encodes up-to-date rules and provisions for all major federal and state public assistance programs, taxes, and tax credits.
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.
There are frameworks available that could inform the standardization of communicating rules as code for U.S. public benefits programs. The Airtable communicates the differences between the frameworks and tools. Each entry is tagged with different categories that identify the type of framework or tool it is.
This article analyses ‘digital distortions’ in Rules as Code, which refer to disconnects between regulation and code that arise from interpretive choices in the encoding process.
We continued Rules as Code Demo Day with Daniel Singer and Preston Cabe from Benefits Data Trust. Benefits Data Trust provides benefit outreach and application assistance services in seven states. Using Benefits Launch, their in-house interview and rules engine, they support two hundred contact center employees as they screen and apply thousands of clients each year. They also offer a self-service screener, Benefits Launch Express. Additionally, they offer an eligibility API to integrate with other services.
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.
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
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.
