This case study details the development of a document extraction prototype to streamline benefits application processing through automated data capture and classification.
This report outlines best practices for developing transparent, accessible, and standardized public sector AI use case inventories across federal, state, and local governments
This toolkit provides guidance to protect participant confidentiality in human services research and evaluation, including legal frameworks, risk assessment strategies, and best practices.
U.S. Department of Health and Human Services (HHS)
This post argues that for the types of large-scale, organized fraud attacks that many state benefits systems saw during the pandemic, solutions grounded in cybersecurity methods may be far more effective than creating or adopting automated systems.
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.
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
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 paper explores how legacy procurement processes in U.S. cities shape the acquisition and governance of AI tools, based on interviews with local government employees.
The state of South Dakota Bureau of Information and Telecommunications (BIT) designed guidelines for the responsible use of AI-generated content in state government agencies, emphasizing the need for proofing, editing, fact-checking, and using AI-generated content as a starting point, not the finished product.
South Dakota Bureau of Information and Telecommunications
