This panel discussion from the Academy's 2025 Policy Summit explores the intersection of artificial intelligence (AI) and public benefits, examining how technological advancements are influencing policy decisions and the delivery of social services.
These fact sheets provide detailed, state-specific data illustrating SNAP's role in reducing hunger and bolstering local economies across the United States
This example examines Miami-Dade County's initiative to streamline digital applications for LIHEAP, aiming to improve accessibility and efficiency for residents seeking energy assistance.
This report explores policy options Utah and other states can adopt to mitigate benefit cliffs, which occur when small income increases lead to sudden loss of public assistance.
This report details the development and deployment of My File, an open-source digital document management tool designed to help individuals securely store and share documents needed to access public services.
This study found that using state-specific names for Medicaid programs increased confusion and reduced both positive and negative opinions about the program.
Errors in administrative processes are costly and burdensome for clients but are understudied. Using U.S. Unemployment Insurance data, this study finds that while automation improves accuracy in simpler programs, it can increase errors in more complex ones.
This video documents the Digital Benefits Network's Digital Identity Community of Practice launch, covering mission review, 2025 goals, California authentication innovations, and peer networking for equitable and effective digital identity in public benefits.
These principles and best practices for AI developers and employers to center the well-being of workers in the development and deployment of AI in the workplace and to value workers as the essential resources they are.
There were over 25 million Medicaid disenrollments in 2023, but national enrollment remained significantly above pre-pandemic levels at over 56 million, with notable state-level variations and near-recovery of child enrollment.
