In this blog post, we’ve detailed some of the steps we take to help capture the best data possible when conducting interviews. This post is intended as a guide for people who need to conduct user interviews and for people simply curious about how we work.
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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
This essay explains why the Center on Privacy & Technology has chosen to stop using terms like "artificial intelligence," "AI," and "machine learning," arguing that such language obscures human accountability and overstates the capabilities of these technologies.
The Policy Rules Database (PRD), developed by the Federal Reserve Bank of Atlanta and the National Center for Children in Poverty, consolidates complex rules for major U.S. federal and state benefit programs and tax policies into a standardized, easy-to-use format. This database allows researchers to model public assistance impacts, simulate policy changes, and analyze benefits cliffs across various household scenarios using common rules and language across different programming platforms.
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.
A comprehensive series of workshops and courses designed to equip public sector professionals with the knowledge and skills to responsibly integrate AI technologies into government operations.​
While millions of workers have gained access to PFML, the lack of uniformity in mandatory PFML programs created a growing patchwork of state laws, differing on nearly 30 policy components across four key areas: substantive benefits, financing, eligibility, and administrative requirements.
In this piece, the Digital Benefits Network shares several sources—from journalistic pieces, to reports and academic articles—we’ve found useful and interesting in our reading on automation and artificial intelligence.
