兰德-数据和人工智能生物设计-与生物训练数据相关的风险和治理机会（英）

ALLISON BERKE, FORREST W. CRAWFORD, TOBY WEBSTER, JAMES SMITH, SANA ZAKARIA, SELLA NEVOData and AI-Enabled Biological DesignRisks Related to Biological Training Data and Opportunities for GovernanceExpert InsightsPERSPECTIVE ON A TIMELY POLICY ISSUEJune 2025For more information on this publication, visit www.rand.org/t/PEA3886-1.About RANDRAND is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. To learn more about RAND, visit www.rand.org.Research IntegrityOur mission to help improve policy and decisionmaking through research and analysis is enabled through our core values of quality and objectivity and our unwavering commitment to the highest level of integrity and ethical behavior. To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other conflicts of interest through staff training, project screening, and a policy of mandatory disclosure; and pursue transparency in our research engagements through our commitment to the open publication of our research findings and recommendations, disclosure of the source of funding of published research, and policies to ensure intellectual independence. For more information, visit www.rand.org/about/research-integrity.RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors.Published by the RAND Corporation, Santa Monica, Calif.© 2025 RAND Corporation is a registered trademark.Limited Print and Electronic Distribution RightsThis publication 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 its webpage on rand.org is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research products for commercial purposes. For information on reprint and reuse permissions, visit www.rand.org/about/publishing/permissions.PE-A3886-1 iii About This Paper In this paper, we assess current knowledge about the link between biological data and the capabilities of artificial intelligence models trained on large volumes of biological data (AI-bio models), describe the anticipated impacts of new biological data sources, and outline potentially dangerous capabilities that could come from broad availability of certain types of biological data. We then recommend strategies to limit the potentially dangerous capabilities arising from biological data, including options for governance of experiments and data creation, governance of curation and aggregations of data, controls on access to collections of data, and gove