This report offers best practices for public agencies implementing digital identity verification, emphasizing privacy, equity, and security in the delivery of government services.
This article advises government agencies to prioritize cybersecurity methods over AI-driven approaches when combating identity fraud in benefits programs, highlighting potential risks that automated systems pose to legitimate applicants.
This resource provides updated draft digital identity guidelines for identity proofing, authentication, and federation, aiming to improve security, privacy, usability, and equity in digital identity systems.
DSN Spotlights are short-form project profiles that feature exciting work happening across our network of digital government practitioners. Spotlights celebrate our members’ stories, lift up actionable takeaways for other practitioners, and put the examples we host in the Digital Government Hub in context.
This memo provides information to child and family service agencies on improving support for intersex children, adolescents, and their families through affirming practices, resources, and partnerships.
U.S. Department of Health and Human Services (HHS)
Executed on September 24, 2024, a memorandum for the heads of executive departments and agencies on advancing the responsible acquisition of artificial intelligence in government.
The Digital Identity Community of Practice kick-off event featured key resources, a new research publication on account creation and identity proofing, and insights from multiple speakers.
This Guide to Artificial Intelligence provides a strategic framework for the ethical and responsible implementation of GenA technologies in state operations.
Colorado Governor's Office of Information Technology (OIT)
This handbook provides local governments with practical guidelines, best practices, and ethical considerations for adopting and using AI tools, emphasizing transparency, human oversight, and risk management.
This report reviews global AI governance tools, highlighting their importance in ensuring trustworthy AI, while identifying gaps and risks in their effectiveness, and offering recommendations to improve their development, oversight, and integration into policy frameworks.
