The State of Connecticut's policy on Artificial Intelligence (AI) Responsible Use establishes a comprehensive framework for the ethical utilization of artificial intelligence in the Connecticut state government.
This report explores innovative solutions and insights from CMS Innovation Center's Hackathon series to address the unique healthcare challenges faced by rural, Tribal, and geographically isolated communities.
This profile provides a cross-sectoral profile of the AI Risk Management Framework specifically for Generative AI (GAI), outlining risks unique to or exacerbated by GAI and offering detailed guidance for organizations to govern, map, measure, and manage those risks responsibly.
National Institute of Standards and Technology (NIST)
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 report analyzes the rise of digital driver’s licenses (DDLs) and warns that, without strong safeguards, they could threaten privacy, civil liberties, and equitable access to identification.
In this presentation, team members from the North Carolina Department of Health and Human Services provide an overview of the implementation process for cross enrollment with SNAP, WIC, and Medicaid in North Carolina.
North Carolina Department of Health and Human Services
This policy brief outlines how improved data sharing between federal agencies, state and local governments, and institutions can leverage existing data from other benefits programs to streamline eligibility processes and benefits uptake for the Affordable Connectivity Program (ACP) and other programs.
The New Mexico Human Services Department and Department of Health, as part of the Coordinating SNAP & Nutrition Supports program, leveraged data sharing to align SNAP, Medicaid, TANF, and WIC.
American Public Human Services Association (APHSA)
This playbook is designed to help government and other key sectors use data sharing to illuminate who is not accessing benefits, connect under-enrolled populations to vital assistance, and make the benefits system more efficient for agencies and participants alike.
