This plan promotes responsible AI use in public benefits administration by state, local, tribal, and territorial governments, aiming to enhance program effectiveness and efficiency while meeting recipient needs.
U.S. Department of Health and Human Services (HHS)
This framework outlines USDA’s principles and approach to support States, localities, Tribes, and territories in responsibly using AI in the implementation and administration of USDA’s nutrition benefits and services. This framework is in response to Section 7.2(b)(ii) of Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.
Professor Don Moynihan discusses how administrative burden is an effective tool to make it difficult for people to access certain types of benefits, noting that this is particularly harmful to communities of color.
This playbook provides federal agencies with guidance on implementing AI in a way that is ethical, transparent, and aligned with public trust principles.
U.S. Department of Health and Human Services (HHS)
This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.
This report explores Michigan’s implementation of the Pandemic Electronic Benefit Transfer (P-EBT) program. Drawing on interviews from individuals within the Michigan Department of Health and Human Services and input from SNAP participants via surveys distributed using the Fresh EBT app, this report provides insights into the strategies that enabled Michigan to roll out an entirely new program quickly and effectively.
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
Californians who receive food assistance come from all backgrounds, but many share a similar story: they were barely getting by financially when they were tipped into crisis by an unexpected expense or loss of income. This site shares their stories.
This report investigates how D.C. government agencies use automated decision-making (ADM) systems and highlights their risks to privacy, fairness, and accountability in public services.
This guide by Cyd Harrell serves as a comprehensive manual for technologists aiming to engage effectively in public sector projects, offering practical advice on navigating government partnerships and driving impactful change.
