The Modern Government Leaders program brought together federal executive innovators to share best practices and drive service delivery and process modernization.
This study assesses five commercial RIdV solutions for equity across demographic groups and finds that two are equitable, while two have inequitable performance for certain demographics.
To assist states in closing digital skill gaps and preparing for digital equity planning, this brief offers key questions and resources for state leaders to consider.
EBT theft has deeply damaged the lives of the lowest-income Americans. The following insights reveal a system that leaves people in the dark and fails to protect a crucial lifeline.
Comprehensive and sustained improvement in benefits access and customer experience requires changes across policy, operations, technology, staffing, procurement, and more. This guide offers a collection of actions and best practices for states to apply.
Usability tests can help teams develop products that are user-centered, accessible, and inclusive. This guide will help you create a robust plan to promote a successful usability test.
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.
A training course on using artificial intelligence (AI) tools to de-jargonize government language, with a tutorial on turning a complex piece of government writing into simpler and easier-to-understand language for government employees and residents alike.
Webinar that shares Nava’s partnership with the Gates Foundation and the Benefits Data Trust that seeks to answer if generative and predictive AI can be used ethically to help reduce administrative burdens for benefits navigators.
Government agencies adopting generative AI tools seems inevitable at this point. But there is more than one possible future for how agencies use generative AI to simplify complex government information.
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.
