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.
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
This research study analyzes the structural and budgetary layout of eleven US-based Digital Service Teams (DSTs) at the municipal, county, and state levels. In doing so, it sets out to answer the research question: “How are digital service teams structured and funded?”
Sheev Davé, Product Manager at Notify.gov (GSA), provides an overview of what makes an effective multilingual translation through the principles of designing tools that have the ability to have conversations, meeting communities where they are, use of plain language and prioritize tricky topics, key life events and using trusted delivery sources.
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.
This presentation explores the balance between security and user experience in digital benefit account creation and authentication, highlighting insights from a forthcoming playbook focused on SNAP and Medicaid portals.
There are frameworks available that could inform the standardization of communicating rules as code for U.S. public benefits programs. The Airtable communicates the differences between the frameworks and tools. Each entry is tagged with different categories that identify the type of framework or tool it is.
This article advises government agencies to prioritize cybersecurity methods over AI-driven approaches when combating identity fraud in benefits programs, highlighting potential risks that automated systems pose to legitimate applicants.
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
