In 2024, the Center on Budget and Policy Priorities and Digital Benefits Network led a workshop to explore key terms related to digital identity, and provide ecosystem-level context on how authentication and identity proofing may show up in the online benefits experience and impact clients. This resource links to the presentation slides.
This book explores the current capabilities, future possibilities, and necessary governance for facial recognition technology. The report discusses legal, societal, and ethical implications of the technology, and recommends ways that federal agencies and others developing and deploying the technology can mitigate potential harms and enact more comprehensive safeguards.
National Academies of Sciences, Engineering, and Medicine
Federal guidelines for digital identity services, outlining technical and procedural requirements for identity proofing, authentication, and federation.
National Institute of Standards and Technology (NIST)
The Digital Benefits Network at the Beeck Center for Social Impact + Innovation at Georgetown University and Public Policy Lab co-hosted a webinar presenting breaking research on beneficiary experiences with digital identity processes in public benefits.
Recapping the work and achievements of the Digital Benefits Network (DBN), Digital Service Network (DSN), and the State Chief Data Officers Network (CDO) in 2025.
Remote identify proofing is the process federal agencies and other entities use to verify that the individuals who apply online for benefits and services are who they claim to be. If the applicant responds correctly to personal questions, their identity is considered to be verified. However, data stolen in recent breaches could be used fraudulently to respond to knowledge-based verification questions. Alternative methods are available that provide stronger security, but these methods may have limitations in cost, convenience, technological maturity, and they may not be viable for all segments of the public.
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
This research brief summarizes the ideas and recommendations from sessions with dozens of cross-sector stakeholders within the technology ecosystem to identify conditions for better, healthier, more secure digital ecosystems that could help guide the next generation of open protocols and platforms.
The Enterprise Single Sign-On (SSO) Playbook is a practical guide to help federal agencies implement or modernize an SSO service for federal employee access to government applications.
This policy brief offers recommendations to policymakers relating to the computational and human sides of facial recognition technologies based on a May 2020 workshop with leading computer scientists, legal scholars, and representatives from industry, government, and civil society
