Learn how to use generative AI to quickly create unemployment insurance translations that are accurate, easy to understand, and tailored to your state.
This case study documents how Civilla partnered with the Michigan Department of Health and Human Services (MDHHS) to redesign and modernize online enrollment for the state’s largest benefit programs.
With a federal accessibility deadline approaching, the Beeck Center convened government leaders to turn compliance pressure into lasting digital inclusion.
This user guide provides step-by-step instructions for families in Iowa to find licensed child care providers online based on location, schedule, and program preferences.
A tool for CDOs advocating for funding, authority, and expansion—and a primer for government leaders unfamiliar with the role. The report establishes shared vocabulary, identifies six CDO office archetypes, and offers cross-state insights on structures, priorities, and challenges.
This article shares insights from Minnesota-based focus groups, revealing that low-income women navigating unemployment insurance often face confusion and uncertainty around eligibility, complex administrative processes, and additional challenges related to childcare, housing stability, and mistrust of benefit systems.
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 assessment that maps how artificial intelligence is currently being used, governed, and managed across local, state, and federal governments in the United States.
A TLDR of the State CDO Archetypes report—covering how state CDO offices operate and the six archetypes that define them. Written for event attendees and government staff: governor's office, IT and budget leadership, legal and data officials, and legislators who oversee CDO funding and establishment.
An overview video describing the Digital Identity Risk Management process outlined in NIST's Digital Identity Guidelines, which organizations can use to develop a risk-based approach to identity management.
