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.
The team conducted experiments to determine whether clients would be responsive to proactive support offered by a chatbot, and identify the ideal timing of the intervention.
This working draft resource helps individuals with aligning their work with the needs of the communities they wish to serve, while reducing the likelihood of harms and risks those communities may face due to the development and deployment of AI technologies.
The study investigates how state agencies administering SNAP comply with Title VI of the Civil Rights Act by providing language access for individuals with limited English proficiency (LEP).
This report celebrates 50 years of improving maternal and child health in the U.S. through WIC and offers advancements, challenges, and solutions for the future.
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
This presentation explores the balance between security and user experience in digital benefit account creation and authentication, highlighting insights from a forthcoming playbook focused on SNAP and Medicaid portals.
Government leaders discuss how to ensure seamless access to public benefits through breaking down silos, user-friendly digital identities, and privacy-focused security measures.
NYC's My File NYC and New Jersey's unemployment insurance system improvements demonstrate how successful digital innovations can be scaled across various programs, leveraging trust-building, open-source technology, and strategic partnerships.
Sarah Bargal provides an overview of AI, machine learning, and deep learning, illustrating their potential for both positive and negative applications, including authentication, adversarial attacks, deepfakes, generative models, personalization, and ethical concerns.
