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.
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.
Government leaders discuss how to ensure seamless access to public benefits through breaking down silos, user-friendly digital identities, and privacy-focused security measures.
This research study analyzes the structural and budgetary layout of eleven US-based Digital Service Teams (DSTs) at the municipal, county, and state levels. In doing so, it sets out to answer the research question: “How are digital service teams structured and funded?”
Presentation covering the findings of a research study analyzing the structural and budgetary layout of of eleven US-based Digital Service Teams (DSTs) at the municipal, county, and state levels.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
Despite Biden administration reversals to prior policies, Urban Institute data from 2023 shows that many immigrant families, especially those with children or mixed status, continue to avoid benefits amid ongoing economic challenges.
This paper discusses the country’s chronic underinvestment in children and resulting outcomes, including new data on poverty rates among young children, is inextricable from the prospects of young children; and the remarkably comprehensive pandemic-era response policies, including which changes contributed most to reducing child poverty.
