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.
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.
Digitizing public benefits policy will make the biggest impact for administrators and Americans, but only if it happens at the highest level of government.
It is frequently assumed that when rules are implemented as code, a rules engine is necessary. However, it is possible for policy people and engineers to effectively work together to code logic that drives technological system without needing a mediating rules engine at all.
Unofficial calculator allowing users to see if they may be eligible for Medicaid, CHIP, or savings on health insurance. The calculator can be embedded on other websites.
The Benefit Cliffs Calculator helps case managers and public benefit recipients to prepare for benefit cliffs (i.e., declines in benefits due to an increase in earnings). It compares the net resources and benefits available to families under different employment scenarios.
The NYC Benefits Screening API provides machine-readable calculations and criteria for benefits screening that power the ACCESS NYC screening questionnaire.
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.
