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.

Militaries seek to harness artificial intelligence for decision advantage. Yet AI systems introduce a new source of uncertainty in the likelihood of technical failures. Such failures could interact with strategic and human factors in ways that lead to miscalculation and escalation in a crisis or conflict. Harnessing AI effectively requires managing these risk trade-offs by reducing the likelihood, and containing the consequences of, AI failures.

CSET Research Analyst Dahlia Peterson testified before the U.S.-China Economic and Security Review Commission at a hearing on "China’s Challenges and Capabilities in Educating and Training the Next Generation Workforce."

Artificial intelligence is so diverse in its range that no simple one-size-fits-all assessment approach can be adequately applied to it. AI systems have a wide variety of functionality, capabilities, and outputs. They are also created using different tools, data modalities, and resources, which adds to the diversity of their assessment. Thus, a collection of approaches and processes is needed to cover a wide range of AI products, tools, services, and resources.

U.S. policies on artificial intelligence education and the AI workforce must grow, cultivate, attract, and retain the world’s best and brightest. Given China’s role as a producer of AI talent, understanding its AI workforce could provide important insight. This report provides an analysis of the AI workforce demand in China using a novel dataset of 6.8 million job postings. It then outlines potential implications along with future reports in this series.

CSET submitted the following comment in response to the National Institute for Standards and Technology's second draft of its AI Risk Management Framework.

Universities are the engines that power the AI talent pipeline, but mounting evidence suggests that U.S. computer science departments do not have enough faculty to meet growing student interest. This paper explores the potential mismatch between supply and demand in AI education, discusses possible causes and consequences, and offers recommendations for increasing teaching capacity at U.S. universities.

Community and technical colleges offer enormous potential to grow, sustain, and diversify the U.S. artificial intelligence (AI) talent pipeline. However, these institutions are not being leveraged effectively. This report evaluates current AI-related programs and the associated number of graduates. The authors find that few AI and AI-related degrees and certificates are being awarded today. They propose five recommendations to address existing challenges and harness the potential of these institutions to train tomorrow’s AI workforce.

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.