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.
This open-source guide and playbook offer practical strategies, tools, and best practices for government employees and contractors to improve digital accessibility and inclusion.
This report outlines state Medicaid program priorities, including expanding access to services, addressing health disparities, and implementing cost-containment measures amid post-pandemic uncertainties.
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.
This session from FormFest 2024 featured the Department of Homeland Security and the United States Digital Service talking about their work to reduce form burdens for internal and external users.
Research identified five key obstacles that researchers, activists, and advocates face in efforts to open critical public conversations about AI’s relationship with inequity and advance needed policies.
This brief outlines research recommendations to better understand and improve income support and employment services for low-income and at-risk LGBT populations.
U.S. Department of Health and Human Services (HHS)
A practical accessibility framework that provides testable heuristics to help designers and developers evaluate and improve the inclusivity of data visualizations and data-driven interfaces.
Usability tests can help teams develop products that are user-centered, accessible, and inclusive. This guide will help you conduct a successful usability test, from coordinating with participants to analyzing your findings.
In this presentation, Pia Andrews explores how open source legislation as code can be a public utility to increase transparency, and enable better implementation and testing of government systems.
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
