On May 19, 2023, the Digital Benefits Network published a new, open dataset documenting authentication and identity proofing requirements across online SNAP, WIC, TANF, Medicaid, child care (CCAP)applications, and unemployment insurance applications. This page includes data and observations about authentication and identity proofing steps specifically for online unemployment insurance applications.
EBT theft has deeply damaged the lives of the lowest-income Americans. The following insights reveal a system that leaves people in the dark and fails to protect a crucial lifeline.
This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.
This resource examines how improvements in customer service experiences in public benefit programs like Medicaid, CHIP, and TANF can help better meet enrollees’ needs and build trust in government.
Little is known about how agencies are currently using AI systems, and little attention has been devoted to how agencies acquire such tools or oversee their use.
In May 2020, Stanford's HAI hosted a workshop to discuss the performance of facial recognition technologies that included leading computer scientists, legal scholars, and representatives from industry, government, and civil society. The white paper this workshop produced seeks to answer key questions in improving understandings of this rapidly changing space.
The Center on Budget and Policy Priorities (CBPP) report discusses how reducing administrative burdens in Medicaid can enhance health outcomes and promote racial equity.
This report explores Michigan’s implementation of the Pandemic Electronic Benefit Transfer (P-EBT) program. Drawing on interviews from individuals within the Michigan Department of Health and Human Services and input from SNAP participants via surveys distributed using the Fresh EBT app, this report provides insights into the strategies that enabled Michigan to roll out an entirely new program quickly and effectively.
The Center for Democracy and Technology's brief clarifies misconceptions about artificial intelligence (AI) in government services, emphasizing the need for precise definitions, awareness of AI's limitations, recognition of inherent biases, and acknowledgment of the significant resources required for effective implementation.
