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.
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 Atlanta Fed’s CLIFF tools provide greater transparency to workers about potential public assistance losses when their earnings increase. We find three broad themes in organization-level implementation of the CLIFF tools: identifying the tar- get population of users; integrating the tool into existing operations; and integrating the tool into coaching sessions.
In this video, Digital Benefits Network Senior Director Ariel Kennan presents highlights from the Cross-Sector Insights From the Rules as Code Community of Practice report released in February 2024. The Cross-Sector Rules as Code Roundtable on February 22, 2024 was hosted by Rules as Code Community of Practice at the Digital Benefits Network.
In this video, Benefits Data Trust Senior Product Manager Daniel Singer and Software Engineering Manager Preston Cabe present their eligibility screening platform including their rules engine, API, and DMN rules notation. The Cross-Sector Rules as Code Roundtable on February 22, 2024 was hosted by Rules as Code Community of Practice at the Digital Benefits Network.
In this video, Policy Engine CEO Max Ghenis provides a demonstration of their product and API and discusses use cases in Rules as Code. The Cross-Sector Rules as Code Roundtable on February 22, 2024 was hosted by Rules as Code Community of Practice at the Digital Benefits Network.
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.
This playbook is a method to apply the National Institute of Standards and Technology (NIST) Special Publication 800-63-3 Digital Identity Guidelines. Federal agencies can perform a Digital Identity Risk Assessment (DIRA) to determine the appropriate identity, authenticator, or federation level outlined to access an application. While this playbook was written for U.S. Federal Agencies, it can be tailored to fit any organization.
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.
This paper describes results from fieldwork conducted at a social services site where the workers evaluate citizens' applications for food and medical assistance submitted via an e-government system. These results suggest value tensions that result - not from different stakeholders with different values - but from differences among how stakeholders enact the same shared value in practice.
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
