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 resource highlights strategies for integrating benefits renewals and correspondence, potentially reducing administrative burdens for both clients and caseworkers.
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
Medicaid and SNAP have reduced racial and ethnic disparities in healthcare access and food security, but some administrative and eligibility policies continue to create inequitable barriers.
This essay explains why the Center on Privacy & Technology has chosen to stop using terms like "artificial intelligence," "AI," and "machine learning," arguing that such language obscures human accountability and overstates the capabilities of these technologies.
During the call, we heard from two speakers: April Dunlap, Policy Administrator for Arizona’s Department of Economic Security and Professor Michele Gilman, Venable Professor of Law and Associate Dean for Faculty Research and Development at the University of Baltimore School of Law.
This article discusses the challenges of today’s centralized identity management and investigates current developments regarding verifiable credentials and digital wallets.
Building on our February 2022 report Benefit Eligibility Rules as Code: Reducing the Gap Between Policy and Service Delivery for the Safety Net, the Beeck Center’s Digital Benefits Network (DBN) recently held a convening to share progress and potential in digitizing benefits eligibility and to begin addressing how a national approach could be started.
Digitizing public benefits policy will make the biggest impact for administrators and Americans, but only if it happens at the highest level of government.
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)
This guidebook aims to equip state and local agencies with the practical insights they need to develop a text messaging outreach program for SNAP recertification.
