Code for America helped expand GetCalFresh (a service that guides Californians through the SNAP application process and helps government deliver food assistance to people in need) from a small pilot into a statewide service. They also recently concluded a similar pilot in Michigan along with Civilla and the Michigan Department of Health and Human Services.
This policy report offers recommendations for improving digital identity practices in the United States, emphasizing the role of government in creating secure, accessible digital identity resources.
This policy brief outlines how improved data sharing between federal agencies, state and local governments, and institutions can leverage existing data from other benefits programs to streamline eligibility processes and benefits uptake for the Affordable Connectivity Program (ACP) and other programs.
It is frequently assumed that when rules are implemented as code, a rules engine is necessary. However, it is possible for policy people and engineers to effectively work together to code logic that drives technological system without needing a mediating rules engine at all.
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.
Governor Kathy Hochul announced a new client feedback initiative in partnership with Code for America to improve New York's WIC program by implementing live online chat to gather input from participants, streamline enrollment, and increase access to healthy food for eligible families.
The second event in the Digital Service Network’s summer event series, Let’s Get Digital, focused on the City of Boston’s transformative journey to streamline its procurement processes.
This article explores how legal documents can be treated like software programs, using methods like software testing and mutation analysis to enhance AI-driven statutory analysis, aiding legal decision-making and error detection.
