This session from FormFest 2024 walked attendees through some of the major changes AI is bringing to form design. Learn about the National Head Start Association’s use of AI to reduce administrative burden and the Canadian Digital Service’s tips for protecting government applications systems from AI.
Nava built flexible and reusable software and design components to make it easier for Vermonters to access their benefits. These components support Vermont’s long-term vision of integrating eligibility and enrollment processes for all of the state’s healthcare and financial benefit programs.
Nava PBC developed a prototype API and digital screener in Montana to streamline eligibility and enhance program access, illustrating how API standards could improve interoperability and modernize WIC systems nationwide.
In this panel conversation from Better Identity Coalition’s 2022 policy forum “Identity, Authentication, and the Road Ahead” presenters from the General Services Administration, the Transportation Security Administration, the Consumer First Coalition, and the congressional branch, discuss government’s role in digital identity.
This article describes the General Services Administration’s efforts to get a limited number of state and local governments to try login.gov with their federally funded programs.
This session from FormFest 2024 features the South Carolina Early Childhood Advisory Council’s work developing a single portal to integrate applications for publicly funded programs and services, and the office of Federal Student Aid’s work on the FAFSA form.
In response to COVID-19, the Workers Lab and Steady developed the "Income Passport" to streamline gig workers' unemployment benefit applications by pulling income data directly from gig platforms and financial accounts. This tool reduced manual verification time, helped prevent fraud, and improved workers' access to full benefits, with successful tests in Alabama and Louisiana demonstrating significant time savings and improved service delivery.
The team examined how AI, specifically LLMs, could streamline the case review process for SNAP applications to alleviate the burden on case workers while potentially improving accuracy.
