In this report, the Strike Team outlines its recommendations and suggested next steps for the EDD to address the backlog and improve on future processing of unemployment claims.
This retrospective looks at the way the NYCOpportunity initiative worked across City government, partnering with agencies to initiate new approaches and enhance city practices. It also highlights key areas of focus for the NYC Opportunity team between 2014 and 2021.
The RFI summary report consolidates submissions received from the open-source software community and details twelve activities that members of the OS3I plan—or have completed—in 2024-2025.
This paper discusses the country’s chronic underinvestment in children and resulting outcomes, including new data on poverty rates among young children, is inextricable from the prospects of young children; and the remarkably comprehensive pandemic-era response policies, including which changes contributed most to reducing child poverty.
In this updated primer, the DBN describes how identity proofing and authentication show up in public benefits applications and outlines equity and security concerns raised by common identity proofing and authentication methods.
SNAP Waivers and Adaptations During the COVID-19 Pandemic: A Survey of State Agency Perspectives in 2020 is a study conducted by the Johns Hopkins Institute for Health and Social Policy (IHSP) based at Johns Hopkins Bloomberg School of Public Health and the American Public Human Services Association (APHSA). This research seeks to understand perspectives from state SNAP administrators on the successes, challenges, and lessons learned from waivers and flexibilities used to preserve equitable access to SNAP during the COVID-19 pandemic. Based on state agency survey responses, this report summarizes key findings from the first calendar year of pandemic response and provides policy considerations for the future of SNAP. This research was supported by Healthy Eating Research, a national program of the Robert Wood Johnson Foundation.
Johns Hopkins Institute for Health and Social Policy
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)
This technical brief uses predictive analytics to identify the primary drivers of SNAP payment error rates (PER) following the implementation of the One Big Beautiful Bill (OBBB).
