This playbook is designed to help government and other key sectors use data sharing to illuminate who is not accessing benefits, connect under-enrolled populations to vital assistance, and make the benefits system more efficient for agencies and participants alike.
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)
The Center for Democracy and Technology's brief clarifies misconceptions about artificial intelligence (AI) in government services, emphasizing the need for precise definitions, awareness of AI's limitations, recognition of inherent biases, and acknowledgment of the significant resources required for effective implementation.
The Michigan Department of Health and Human Services, together with the Food Bank Council of Michigan and the Michigan Department of Education developed a comprehensive Food Insecurity Map and a closed-loop referral system for nutrition and economic supports.
American Public Human Services Association (APHSA)
Companies have been developing and using artificial intelligence (AI) for decades. But we've seen exponential growth since OpenAI released their version of a large language model (LLM), ChatGPT, in 2022. Open-source versions of these tools can help agencies optimize their processes and surpass current levels of data analysis, all in a secure environment that won’t risk exposing sensitive information.
The Commonwealth of Pennsylvania's Executive Order 2023-19: Expanding and Governing the Use of Generative Artificial Intelligence Technologies Within the Commonwealth of Pennsylvania
