This data brief uses procurement records published by the U.S. Department of Defense and China’s People’s Liberation Army between April and November of 2020 to assess, and, where appropriate, compare what each military is buying when it comes to artificial intelligence. We find that the two militaries are prioritizing similar application areas, especially intelligent and autonomous vehicles and AI applications for intelligence, surveillance and reconnaissance.

Read our translation of a document that announces China’s 2023 research priorities in explainable and generalizable artificial intelligence methods. It also provides state funds for projects related to these priorities, and explains how Chinese AI researchers can apply for funding. Applications of AI in medicine, biology, physics, materials science, and mathematics are prominent among the 2023 priorities.

Foundation models could eventually introduce several pathways for undermining state security: accidents, inadvertent escalation, unintentional conflict, the proliferation of weapons, and the interference with human diplomacy are just a few on a long list. The Confidence-Building Measures for Artificial Intelligence workshop hosted by the Geopolitics Team at OpenAI and the Berkeley Risk and Security Lab at the University of California brought together a multistakeholder group to think through the tools and strategies to mitigate the potential risks introduced by foundation models to international security.

Identifying emerging technologies is critical to governments, the private sector, and researchers, but these groups lack a shared analytical approach when it comes to assessing the trajectories of new technologies. To better calibrate efforts to protect and promote emerging technologies, supply chain security research provides a mature, relevant analytical framework. This report offers policymakers a template to map emerging technology supply chains using two tools developed by CSET's Emerging Technology Observatory: the Map of Science and the Supply Chain Explorer.

Technical talent is vital to innovation and economic growth, and attracting these highly mobile workers is critical to staying on the cutting-edge of the technological frontier. Conventional wisdom holds that the defense community generally struggles to access this talent pool. This policy brief uses LinkedIn data to track the movement of tech workers between industries and metro areas, with a particular focus on the U.S. Department of Defense, the defense industrial base, and the so-called “Big Tech” companies.

CSET submitted the following comment in response to a Request for Information (RFI) from the National Science Foundation (NSF) about the development of the newly established Technology, Innovation, and Partnerships (TIP) Directorate, in accordance with the CHIPS and Science Act of 2022.

Real-world harms caused by the use of AI technologies are widespread. Tracking and analyzing them improves our understanding of the variety of harms and the circumstances that lead to their occurrence once AI systems are deployed. This report presents a standardized conceptual framework for defining, tracking, classifying, and understanding harms caused by AI. It lays out the key elements required for the identification of AI harm, their basic relational structure, and definitions without imposing a single interpretation of AI harm. The brief concludes with an example of how to apply and customize the framework while keeping its modular structure.

Jenny Jun's testimony before the House Foreign Affairs Subcommittee on Indo-Pacific for a hearing titled, "Illicit IT: Bankrolling Kim Jong Un."

This data brief identifies the most influential AI researchers in the United States between 2010 and 2021 via three metrics: number of AI publications, citations, and AI h-index. It examines their demographic profiles, career trajectories, and research collaboration rates, finding that most are men in the later stages of their career, largely concentrated in 10 elite universities and companies, and that nearly 70 percent of America’s top AI researchers were born abroad.

Artificial intelligence-enabled systems are transforming society and driving an intense focus on what policy and technical communities can do to ensure that those systems are trustworthy and used responsibly. This analysis draws on prior work about the use of trustworthy AI terms to identify 18 clusters of research papers that contribute to the development of trustworthy AI. In identifying these clusters, the analysis also reveals that some concepts, like "explainability," are forming distinct research areas, whereas other concepts, like "reliability," appear to be accepted as metrics and broadly applied.