This issue brief uses data from the National Science Foundation’s Survey of Doctorate Recipients to explore how many of the international students who earn STEM PhDs from U.S. universities stay in the country after graduation. The authors trace the journeys that these graduates take through the immigration system and find that most remain in the United States long after earning their degrees.

Problems of AI safety are the subject of increasing interest for engineers and policymakers alike. This brief uses the CSET Map of Science to investigate how research into three areas of AI safety — robustness, interpretability and reward learning — is progressing. It identifies eight research clusters that contain a significant amount of research relating to these three areas and describes trends and key papers for each of them.

​​This Classifying AI Systems Interactive presents several AI system classification frameworks developed to distill AI systems into concise, comparable and policy-relevant dimensions. It provides key takeaways and framework-specific results from CSET’s analysis of more than 1,800 system classifications done by survey respondents using the frameworks. You can explore the frameworks and example AI systems used in the survey, and even take the survey.

The strength of a country’s talent pipeline depends in no small part on the quality of its universities. This data brief explores how Chinese and U.S. universities perform in two different global university rankings, why their standings have changed over time, and what those trends mean for graduates.

In the past decade, Chinese researchers have become increasingly prolific authors of highly cited AI publications, approaching the global research share of their U.S. counterparts. However, some analysts question the impact of Chinese publications; are they well respected internationally, and do they cover important topics? In this data brief, the authors build on prior analyses of top AI publications to provide a richer understanding of the two countries’ contributions to high-impact AI research.

This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.

This research agenda provides a roadmap for the next phase of CSET’s line of research on the U.S. AI workforce. Our goal is to assist policymakers and other stakeholders in the national security community to create policies that will ensure the United States maintains its competitive advantage in AI talent. We welcome comments, feedback and input on this vision at cset@georgetown.edu.

This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.

Created through a joint partnership between CSET and the AI Education Project, the AI Education Catalog aims to raise awareness of the AI-related programs available to students and educators, as well as to help inform AI education and workforce policy.

This policy brief addresses the need for a clearly defined artificial intelligence education and workforce policy by providing recommendations designed to grow, sustain, and diversify the U.S. AI workforce. The authors employ a comprehensive definition of the AI workforce—technical and nontechnical occupations—and provide data-driven policy goals. Their recommendations are designed to leverage opportunities within the U.S. education and training system while mitigating its challenges, and prioritize equity in access and opportunity to AI education and AI careers.