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.
Recording of GOVChats hosted by GTA's Digital Services Georgia, where speakers dive into the artificial intelligence (AI) programs and initiatives unfolding across the states of Georgia, Maryland, and Vermont.
This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.
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.
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.
A workshop led by Elham Ali on integrating the principles of human-centered design and equity to Artificial Intelligence (AI) design, use, and evaluation.
The State of California government published guidelines for the safe and effective use of Generative Artificial (GenAI) within state agencies, in accordance with Governor Newsom's Executive Order N-12-23 on Generative Artificial Intelligence.
This toolkit provides guidance to protect participant confidentiality in human services research and evaluation, including legal frameworks, risk assessment strategies, and best practices.
U.S. Department of Health and Human Services (HHS)
The team developed an AI solution to assist benefit navigators with in-the-moment program information, finding that while LLMs are useful for summarizing and interpreting text, they are not ideal for implementing strict formulas like benefit calculations, but can accelerate the eligibility process by leveraging their strengths in general tasks.
