The team introduced an AI assistant for benefits navigators to streamline the process and improve outcomes by quickly assessing client eligibility for benefits programs.
Through deeply reported case studies and insights from focus groups, this report provides an in-depth look at the impact of pandemic-era government spending on families.
This case study examines how Michigan’s Department of Health and Human Services uses data practices to advance racial equity in child welfare through identity-informed data collection and anonymous decision-making.
U.S. Department of Health and Human Services (HHS)
This report analyzes the current state of digital identity in the United States, outlines challenges such as privacy concerns, fragmented systems, and lack of standards, and proposes policy and technology solutions to build a secure, interoperable, and user-friendly national digital identity framework.
Information Technology & Innovation Foundation (ITIF)
This collection of research references is designed to support government agencies designing public-facing identity management processes that meet people’s needs.
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 resources + examples we host in the Digital Government Hub in context.Â
This brief estimates of benefits, costs, interactions with other means tested programs, and impact on poverty for the paid family and medical leave program.
This report offers best practices for public agencies implementing digital identity verification, emphasizing privacy, equity, and security in the delivery of government services.
The Digital Benefit Network's Digital Identity Community of Practice held a session to hear considerations from civil rights technologists and human-centered design practitioners on ways to ensure program security while simultaneously promoting equity, enabling accessibility, and minimizing bias.
