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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.

This brief examines trends in patents generated through federally funded research, otherwise known as Bayh-Dole patents. We find that while Bayh-Dole patents make up a small proportion of U.S. patents overall, they are much more common in certain fields, especially in biosciences and national defense related fields. Academic institutions are major recipients of Bayh-Dole patents, and the funding landscape for patent-producing research has shifted since Bayh-Dole came into effect in 1980.

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.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This short series explores how government procurement data can shed light on federal technological interest and utilization. It analyzes contract metadata, provided in a collaborative project with Govini, to track key emerging technologies through the federal procurement process.

The choice of method for surfacing AI-relevant publications impacts the ultimate research findings. This report provides a quantitative analysis of various methods available to researchers for identifying AI-relevant research within CSET’s merged corpus, and showcases the research implications of each method.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This short series explores how government procurement data can shed light on federal technological interest and utilization. It analyzes contract metadata, provided in a collaborative project with Govini, to track key emerging technologies through the federal procurement process.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This five-part series uses data from the U.S. Department of Education and other select sources to complement existing CSET work on the U.S. AI workforce.

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Homeland Security Subcommittee on Cybersecurity, Infrastructure Protection, and Innovation at a hearing on "Securing the Future: Harnessing the Potential of Emerging Technologies While Mitigating Security Risks." Lohn discussed the application of AI systems in cybersecurity and AI’s vulnerabilities.