Concerns over risks from generative artificial intelligence systems have increased significantly over the past year, driven in large part by the advent of increasingly capable large language models. But, how do AI developers attempt to control the outputs of these models? This primer outlines four commonly used techniques and explains why this objective is so challenging.
Center for Security and Emerging Technology (CSET)
Louisiana issued an RFI to identify solutions that can provide a technology platform for determining eligibility and managing cases across multiple human services programs.
The Ethical Artificial Intelligence (AI) Policy of the City of Tempe establishes principles and governance structures to ensure the responsible, fair, and transparent use of AI in municipal operations.
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.
The team introduced an AI assistant for benefits navigators to streamline the process and improve outcomes by quickly assessing client eligibility for benefits programs.
This report reviews global AI governance tools, highlighting their importance in ensuring trustworthy AI, while identifying gaps and risks in their effectiveness, and offering recommendations to improve their development, oversight, and integration into policy frameworks.
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.
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).
