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 Policy2Code Prototyping Challenge explored utilizing generative AI technology to translate U.S. government policies for public benefits into plain language and code, culminating in a Demo Day where twelve teams showcased their projects for feedback and evaluation.
Governor Kathy Hochul announced a new client feedback initiative in partnership with Code for America to improve New York's WIC program by implementing live online chat to gather input from participants, streamline enrollment, and increase access to healthy food for eligible families.
The article highlights the growing issue of SNAP benefit theft through skimming and advocates for permanent security measures and benefit replacements to protect vulnerable households.
This fact sheet outlines the key principles for designing an effective Child Tax Credit that reduces child poverty, supports working families, promotes racial and economic equity, and delivers long-term benefits for children and the economy.
Guidance on improving how well AI systems can understand digital content. It emphasizes using machine-readable formats and applying clear content design strategies to enhance both AI processing and human accessibility
This 11x17 service blueprint visualizes every step, system, and policy decision involved in implementing Medicaid work requirements under H.R. 1—from application to renewal—identifying pain points, questions, and opportunities for states to streamline and humanize the process
This blog introduces Code for America’s new service blueprint for Medicaid work requirements, highlighting how it can help states map system changes, identify pain points, and prioritize human-centered design.
This guide outlines key strategies, definitions, and procedures for improving SNAP payment accuracy and reducing quality control (QC) error rates across states.
A practical, research-based handbook from The Lab @ DC that teaches public servants how to redesign confusing government forms through user-centered, evidence-based design methods.
