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 report analyzes the rise of digital driver’s licenses (DDLs) and warns that, without strong safeguards, they could threaten privacy, civil liberties, and equitable access to identification.
This report examines Georgia’s Medicaid demonstration testing work requirements—the only such active program in the nation—and provides detailed findings on administrative costs, implementation challenges, and federal oversight weaknesses.
A report that reviews what has been learned from guaranteed income pilot projects in Massachusetts and situates those findings within the broader national evidence base.
This report outlines the U.S. Department of Labor’s comprehensive action plan to strengthen the unemployment insurance (UI) system by addressing chronic underfunding and proposing legislative reforms to support long-term modernization and resilience.
Drawing on interviews and convenings with experts and practitioners from the field of public interest technology, this report contains recommendations across five core priority action areas for cross-sector innovation and collaboration to improve state benefits systems through procurement practices.
This report shares the results of our comprehensive content audit and heuristic evaluation of eligibility pre-screeners, including ratings on security, mobile-friendly design, accessibility, and more.
This report highlights 5 key takeaways from the Aspen Institute Financial Security Program's 2022 Benefits Forum, where 55 experts from various sectors discussed solutions for improving public and private benefits to better support workers and their families.
The Sprint 2 Report: Michigan UI Claimant Experience by Civilla and New America examines challenges in Michigan’s unemployment insurance (UI) system and provides human-centered design recommendations to improve accessibility, clarity, and user experience.
Concerns over risks from generative artificial intelligence systems have increased significantly over the past year, driven in large part by the advent of increasingly capable large language models. But, how do AI developers attempt to control the outputs of these models? This primer outlines four commonly used techniques and explains why this objective is so challenging.
Center for Security and Emerging Technology (CSET)
