This brief outlines the U.S. federal government’s framework to identify, reduce, and address administrative burdens through a series of executive orders, legislative actions, and updated policies focused on improving customer experience and increasing access to government benefits.
A collaborative resource document detailing the civic tech support offerings, state and local government resources, and civic tech organizational support.
This is the summary version of a report that 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.
An updated guide for public sector and civic data users to embed racial equity and community voice throughout the data life cycle—from planning to dissemination.
This paper explores how legacy procurement processes in U.S. cities shape the acquisition and governance of AI tools, based on interviews with local government employees.
This report presents new national survey data showing how benefits cliffs and asset limits negatively affect the economic mobility of low-wage workers in the U.S.
This article examines the concept of "viral cash" and suggests that the future growth of basic income programs will depend on advocacy networks rather than traditional policy diffusion across jurisdictions.
This book is an in-depth exploration of federal programs and controversial legislation demonstrating that administrative burden has long existed in policy design, preventing citizens from accessing fundamental rights. Further discussion of how policymakers can minimize administrative burden to reduce inequality, boost civic engagement, and build an efficient state.
The DBN’s Rules as Code Community of Practice (RaC CoP) creates a shared learning and exchange space for people working on public benefits eligibility and enrollment systems — and specifically people tackling the issue of how policy becomes software code. The RaC CoP brings together cross-sector experts who share approaches, examples, and challenges. Participants are from state, local, tribal, territorial, and federal government agencies, nonprofit organizations, academia, and private sector companies. We host recurring roundtable conversations and an email group for asynchronous updates, insights, and assistance.
This study examines the adoption and implementation of AI chatbots in U.S. state governments, identifying key drivers, challenges, and best practices for public sector chatbot deployment.
This brief analyzes the current state of federal and state government communication around benefits eligibility rules and policy and how these documents are being tracked and adapted into code by external organizations. This work includes comparisons between coded examples of policy and potential options for standardizing code based on established and emerging data standards, tools, and frameworks.
