The Carnegie Classification of Institutions of Higher Education is simplifying its top-tier R1 research criteria this year. Recognizing the strategic importance of historically Black colleges and universities, Congress passed Section 223 of the 2023 National Defense Authorization Act to increase defense research capacity by encouraging the most eligible among these institutions to seek the highly coveted R1 status. This in-depth analysis examines the 2025 classification changes, their effect on eligible HBCUs, and strategies for Congress to maintain progress.

In response to the Office of Science and Technology Policy's request for input on an AI Action Plan, CSET provides key recommendations for advancing AI research, ensuring U.S. competitiveness, and maximizing benefits while mitigating risks. Our response highlights policies to strengthen the AI workforce, secure technology from illicit transfers, and foster an open and competitive AI ecosystem.

As artificial intelligence permeates the economy, the demand for AI talent with all levels of educational attainment will expand in kind. Apprenticeships are an effective education and training pathway for other industries, but are they suitable for AI-related roles? This report analyzes trends in AI-related apprenticeships across the United States from 2013 through 2023. It explores the growth of these programs, completion rates, demographic and geographic information, and the organizations sponsoring these programs.

The emergence of artificial intelligence as a general-purpose technology could profoundly transform work across industries, potentially affecting a variety of occupations. While previous technological shifts largely enhanced productivity and wages for white-collar workers but led to displacement pressures for blue-collar workers, AI may significantly disrupt both groups. This report examines the changing landscape of workforce development, highlighting the crucial role of community colleges, alternative career pathways, and AI-enabled training solutions in preparing workers for this transition.

Automation bias is a critical issue for artificial intelligence deployment. It can cause otherwise knowledgeable users to make crucial and even obvious errors. Organizational, technical, and educational leaders can mitigate these biases through training, design, and processes. This paper explores automation bias and ways to mitigate it through three case studies: Tesla’s autopilot incidents, aviation incidents at Boeing and Airbus, and Army and Navy air defense incidents.

In February 2024, CSET introduced its new cybersecurity jobs dataset, a novel resource comprising ~1.4 million LinkedIn profiles of current U.S. cybersecurity workers. This data snapshot uses the dataset to identify top-producing institutions of cybersecurity talent.

This data snapshot is the first in a series on CSET’s cybersecurity jobs data, a new dataset created by classifying data from 513 million LinkedIn user profiles. Here, we offer an overview of its creation and explore some use cases for analysis.

Understanding the current state of the AI workforce is essential as the U.S. prepares an AI-ready workforce. This Data Snapshot provides the latest estimates for the AI workforce by using data from the U.S. Census Bureau’s 2022 American Community Survey. It also highlights the changes in size and composition of the AI workforce since our earlier analysis of data from 2018.

CSET submitted the following comment in response to a DHS Notice on Proposed Rule-Making from the U.S. Citizenship and Immigration Services about modernizing H-1B requirements, providing flexibility in the F-1 program, and program improvements affecting other nonimmigrant workers

CSET submitted the following comment in response to a Request for Comment (RFC) from the Office of Management and Budget (OMB) about a draft memorandum providing guidance to government agencies regarding the appointment of Chief AI Officers, Risk Management for AI, and other processes following the October 30, 2023 Executive Order on AI.