理解人工智能病原体设计的理论局限性-德尔菲研究的见解（英）

DAVID MANHEIM, ADELINE WILLIAMS, CASEY AVEGGIO, ALLISON BERKEUnderstanding the Theoretical Limits of AI-Enabled Pathogen DesignInsights from a Delphi StudyResearch ReportFor more information on this publication, visit www.rand.org/t/RRA4087-1.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.Cover: Composite design by Carol Ponce adapted from images by Cybrain/Getty Images, Matejmo/Getty Images, Fatido/Getty Images and Zffoto/Getty Images. 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.RR-A4087-1 iii About This Report There are many possible biological risks to human health and global security, including emerging infectious diseases, bioengineered pathogens, and accidental lab leaks. To analyze the probabilities that these threats will occur, it is necessary to consider and resolve uncertainties about the most likely or most concerning risks, risk actors, and enabling technologies. Otherwise, a lack of clarity on what is and is not possible could lead to worst-case catastrophizing that can derail research progress or lead policymakers astray. This has been particularly true for applications of artificial