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.
What exactly are the differences between generative AI, large language models, and foundation models? This post aims to clarify what each of these three terms mean, how they overlap, and how they differ.
Center for Security and Emerging Technology (CSET)
This report examines how implementing Asset Verification Systems (AVS) can streamline Medicaid eligibility determinations for seniors and individuals with disabilities by automating the verification of applicants' financial assets.
This report explores how AI is currently used, and how it might be used in the future, to support administrative actions that agency staff complete when processing customers’ SNAP cases. In addition to desk and primary research, this brief was informed by input from APHSA’s wide network of state, county, and city members and national partners in the human services and related sectors.
American Public Human Services Association (APHSA)
This report summarizes progress made with agencies and members of the public to identify and reduce burdens that individuals, families, and small businesses face every day when interacting with government programs.
This Urban Institute article argues that poverty is driven by structural barriers rather than individual choices and advocates for safety net programs that address systemic inequities.
This UN report warns against the risks of digital welfare systems, emphasizing their potential to undermine human rights through increased surveillance, automation, and privatization of public services.
Drawing on interviews and convenings with experts and practitioners from the field of public interest technology, this report contains recommendations across five core priority action areas for cross-sector innovation and collaboration to improve state benefits systems through procurement practices.
The existing system for evaluating state safety net programs does not adequately capture the human experience of accessing services. This new National Safety Net Scorecard is a more meaningful set of metrics that can effectively asses the true state of the current program delivery landscape and measure progress over time, creating a more human-centered safety net.
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)
