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 explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.
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)
In 2018 the Better Identity Coalition released a Policy Blueprint outlining five key initiatives that to solve the majority of America’s challenges in the digital identity space. This report from 2024 grades progress on each of the original Blueprint’s five key initiatives – as well as the 19 items that were contained in the “action plan” to support those initiatives.
Artificial intelligence promises exciting new opportunities for the government to make policy, deliver services and engage with residents. But government procurement practices need to adapt if we are to ensure that rapidly-evolving AI tools meet intended purposes, avoid bias, and minimize risks to people, organizations, and communities. This report lays out five distinct challenges related to procuring AI in government.
The New Mexico Human Services Department and Department of Health, as part of the Coordinating SNAP & Nutrition Supports program, leveraged data sharing to align SNAP, Medicaid, TANF, and WIC. Their new online interface automates the referral process, making it easier for families to access the nutrition and economic supports they are eligible for.
American Public Human Services Association (APHSA)
The New Jersey Department of Human Services and New Jersey Department of Health collaborated in their Coordinating SNAP & Nutrition Supports project to enhance the enrollment and coordination of SNAP and WIC programs. By developing the NJ Nutritional Data Hub and an innovative webservice, the project identified and reached out to families receiving SNAP but not WIC, and vice versa, significantly streamlining the adjunctive eligibility process. This report documents best practices and lessons learned from their project.
American Public Human Services Association (APHSA)
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. The goal of these initiatives was to leverage cross-sector data to inform policy decisions, streamline access to food assistance, and reduce administrative burden. This report documents lessons learned and outcomes of their project.
American Public Human Services Association (APHSA)
Through the Coordinating SNAP & Nutrition Supports program, Mecklenburg County, NC leveraged a Food Security Navigator model and data analysis to increase access to nutrition supports. This report documents best practices and lessons learned from their project.
American Public Human Services Association (APHSA)
This report highlights best practices and lessons learned from Kansas' partnership with Delivering Change to improve SNAP and WIC access in seven counties through innovative data sharing and targeted outreach.
American Public Human Services Association (APHSA)
