Recent government directives, international conferences, and media headlines reflect growing concern that artificial intelligence could exacerbate biological threats. When it comes to biorisk, AI tools are cited as enablers that lower information barriers, enhance novel biothreat design, or otherwise increase a malicious actor’s capabilities. In this explainer, CSET Biorisk Research Fellow Steph Batalis summarizes the state of the biorisk landscape with and without AI.

CSET submitted the following comment in response to a Request for Comment (RFC) from the Office of Management and Budget (OMB) about a draft memorandum providing guidance to government agencies regarding the appointment of Chief AI Officers, Risk Management for AI, and other processes following the October 30, 2023 Executive Order on AI.

Antimicrobial resistance (AMR) is one of the world’s most pressing global health threats. Basic research is the first step towards identifying solutions. This brief examines the AMR research landscape since 2000, finding that the amount of research is increasing and that the U.S. is a leading publisher, but also that novel solutions like phages and synthetic antimicrobial production are a small portion of that research.

AI capabilities are evolving quickly and pose novel—and likely significant—risks. In these rapidly changing conditions, how can policymakers effectively anticipate and manage risks from the most advanced and capable AI systems at the frontier of the field? This Roundtable Report summarizes some of the key themes and conclusions of a July 2023 workshop on this topic jointly hosted by CSET and Google DeepMind.

How can policymakers credibly reveal and assess intentions in the field of artificial intelligence? Policymakers can send credible signals of their intent by making pledges or committing to undertaking certain actions for which they will pay a price—political, reputational, or monetary—if they back down or fail to make good on their initial promise or threat. Talk is cheap, but inadvertent escalation is costly to all sides.

CSET submitted the following comment in response to a Request for Information (RFI) from the White House's Office of Science and Technology Policy about potential changes to the Policies for Federal and Institutional Oversight of Life Sciences Dual Use Research of Concern (DURC) and Recommended Policy Guidance for Departmental Development of Review Mechanisms for Potential Pandemic Pathogen Care and Oversight (P3CO).

Australia, Canada, Japan, the United Kingdom, and the United States emphasize principles of accountability, explainability, fairness, privacy, security, and transparency in their high-level AI policy documents. But while the words are the same, these countries define each of these principles in slightly different ways that could have large impacts on interoperability and the formulation of international norms. This creates, what we call the “Inigo Montoya problem” in trustworthy AI, inspired by "The Princess Bride" movie quote: “You keep using that word. I do not think it means what you think it means.”

This explainer overviews techniques to produce smaller and more efficient language models that require fewer resources to develop and operate. Importantly, information on how to leverage these techniques, and many of the subsequent small models, are openly available online for anyone to use. The combination of both small (i.e., easy to use) and open (i.e., easy to access) could have significant implications for artificial intelligence development.

Gain- and loss-of-function research have contributed to breakthroughs in vaccine development, genetic research, and gene therapy. At the same time, a subset of gain- and loss-of-function studies involve high-risk, highly virulent pathogens that could spread widely among humans if deliberately or unintentionally released. In this report, we map the gain- and loss-of-function global research landscape using a quantitative approach that combines machine learning with subject-matter expert review.

Artificial intelligence that makes news headlines, such as ChatGPT, typically runs in well-maintained data centers with an abundant supply of compute and power. However, these resources are more limited on many systems in the real world, such as drones, satellites, or ground vehicles. As a result, the AI that can run onboard these devices will often be inferior to state of the art models. That can affect their usability and the need for additional safeguards in high-risk contexts. This issue brief contextualizes these challenges and provides policymakers with recommendations on how to engage with these technologies.