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.

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.

China has poured resources into improving the resilience of its space architecture. But how much progress has Beijing made? This issue brief analyzes China’s space resilience efforts and identifies areas where the United States may need to invest to keep pace.

An expert assessment of Chinese scientific literature validates China's public claim to be working toward artificial general intelligence (AGI). At a time when other nations are contemplating safeguards on AI research, China’s push toward AGI challenges emerging global norms, underscoring the need for a serious open-source monitoring program to serve as a foundation for outreach and mitigation.

ETO’s Open-source software Research and Community Activity (ORCA) tool compiles data on open-source software (OSS) used in science and technology research.