Download Free Electronic Document

FormatFile SizeNotes
PDF file 0.1 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

The rapid evolution of artificial intelligence (AI) technology offers immense opportunity to advance human welfare but also poses novel threats. Foundation models (FMs), unlike other AI, are trained on large datasets that show competence across a wide variety of domains and tasks, such as generating text, audio, video, and images. The generalized competence of FMs is the root of both their potential benefits and their potential risks. In terms of potential positive outcomes, FMs could provide benefits across a wide variety of sectors, such as education. In terms of potential negative outcomes, FMs could allow the creation of chemical and biological weapons or amplify disinformation campaigns that undermine democratic elections.

RAND and the Carnegie Endowment for International Peace hosted a convening on FMs on November 10–12, 2023. The convening covered such topics as model cybersecurity, developer ethical norms, international governance, legal liability, threat assessment, and reporting and other forms of information-sharing. These conference proceedings capture the industry, academic, and think-tank perspectives emerging from the workshops to inform government, civil society, industry, and the broader public discussion about artificial intelligence safety and security.

Research conducted by

Funding for this work was provided by gifts from RAND supporters and income from operations and conducted within the Technology and Security Policy Center of RAND Global and Emerging Risks.

This report is part of the RAND conference proceeding series. RAND conference proceedings present a collection of papers delivered at a conference or a summary of the conference.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.