To kick off the Digital Service Network’s (DSN) summer event series, Let’s Get Digital, the DSN heard from the State of Arizona on their efforts to transform government services through a strong collaboration between information technology (IT) and digital service functions.
An event recap from one of FormFest 2024's breakout sessions featuring speakers from Digital Service Teams across the United States and the Department of Veteran Affairs.
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
A comprehensive analysis of how government digital service teams document and communicate their impact across federal, state, and local levels. This report aims to identify key reporting trends and practices to help teams develop impact narratives that demonstrate their value to stakeholders.
This piece highlights promising design patterns for account creation and identity proofing in public benefits applications. The publication also identifies areas where additional evidence, resources, and coordinated federal guidance may help support equitable implementations of authentication and identity proofing, enabling agencies to balance access and security.
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 primer introduces two foundational software types that can support organizations that are committed to accessible benefits information: content management systems (CMS) and application program interfaces (APIs).
In this policy brief and video, Michele Gilman summarizes evidence-based recommendations for better structuring public participation processes for AI, and underscores the urgency of enacting them.
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
