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 his article featured in the Council of Foreign Relations, Matthias Oschinski shared his expert analysis on the importance of strengthening the United States' STEM talent pipeline.

Zachary Arnold provided his expert insights in an article published by Semafor that discusses a recent analysis by CSET's Emerging Technology Observatory, which highlights the top 30 AI leaders in the S&P 500, ranking companies based on AI patents, workforce size, and research output.

In his op-ed featured in DefenseScoop, Jaret C. Riddick provides his expert analysis on the Great Power Competition.

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.

“AI Chips: What They Are and Why They Matter,“ a report by CSET, was referenced in a Business Insider article. The article explores the urgent need in the US for more workers skilled in building AI chips. It highlights a significant decline in the American semiconductor workforce over the past two decades.

Over the past year, artificial intelligence has quickly become a focal point in K-12 education. This blog post describes new and existing K-12 AI education efforts so that U.S. policymakers and other decision-makers may better understand what’s happening in practice.

The Carnegie Classification of Institutions of Higher Education is making changes to drastically simplify the criteria that determine its highly coveted R1 top-tier research classification. Last year, CSET Senior Fellow, Jaret Riddick, wrote about a new law from Congress, Section 223 of the 2023 National Defense Authorization Act, intended to leverage existing Carnegie classification criteria to increase defense research capacity for historically Black colleges and universities. Now, research is needed to understand how the changes proposed for 2025 classification criteria impact U.S. Department of Defense goals for eligible HBCU partners.

A CSET data snapshot was cited by CNBC in an article that focuses on the increasing prevalence of AI-specific degree programs, driven by the high demand for AI skills in the job market.

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.