PolicyEngine US is a Python-based microsimulation model of the US tax and benefit system. It models federal individual income taxes (including credits), major benefit programs, and state income taxes (currently in six states). The PolicyEngine US package can be used as a Python package, via the PolicyEngine API, or via the policyengine.org web app.
A guide from the General Service Administration to help government decision makers clearly see what AI means for their agencies and how to invest and build AI capabilities.
The toolkit provides strategies for state and local WIC agencies to enhance enrollment by utilizing data from Medicaid and SNAP for cross-program data matching and targeted outreach.
A workshop led by Elham Ali on integrating the principles of human-centered design and equity to Artificial Intelligence (AI) design, use, and evaluation.
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 paper outlines the need for comprehensive reforms to improve the U.S. government's capacity to effectively implement policies, focusing on reducing bureaucratic inefficiencies, enhancing workforce structures, and leveraging digital infrastructure.
This report highlights the agency's role in transforming federal digital services through human-centered design, agile technology, and cross-agency collaboration.
This provides a comprehensive look at child well-being across the U.S., ranking states and highlighting policy recommendations to improve outcomes for children.
This is a modular, dynamic roadmap guides the U.S. HHS's ongoing implementation of open data policies while inviting public collaboration and feedback.
U.S. Department of Health and Human Services (HHS)
This report examines how governments use AI systems to allocate public resources and provides recommendations to ensure these tools promote equity, transparency, and fairness.
