These guidelines provide UK government organizations with best practices for responsibly and effectively procuring artificial intelligence (AI) systems.
This report examines how the U.S. federal government can enhance the efficiency and equity of benefit delivery by simplifying eligibility rules and using a Rules as Code approach for digital systems.
Drawing on the Beeck Center’s research on government, nonprofit, academic, and private sector organizations that are working to improve access to safety net benefits, this report highlights best practices for creating accessible benefits content.
In this video, Susan S. Gibson, chair of the Pandemic Response Accountability Committee's (PRAC) Identity Fraud and Redress Working Group, speaks with Jeremy Grant of the Better Identity Coalition, about the challenges of identity fraud for benefits program during the COVID-19 pandemic.
Handbook by 18F designed for executives, budget specialists, legislators, and other “non-technical” decision-makers who fund or oversee state government technology projects that receive federal funding and implement the necessary technology to support federal programs. It aids in setting projects up for success by asking the right questions, identifying the right outcomes, and equally important, empowering decision-makers with a basic knowledge of the fundamental principles of modern software design.
This guide is intended to provide a general overview of the national statutory and regulatory landscape governing the legality of sending large volumes of text messages and sharing client information.
APHSA explains how certain tools and recommendations about when people apply for help, engage in services, and maintain benefits can have a powerful effect to either counter or exacerbate structural barriers to accessing assistance.
American Public Human Services Association (APHSA)
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
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)
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)
