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.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This three-part series uses data from a variety of sources to track how three cloud providers—Amazon, Alphabet, and Microsoft—distribute their financial resources to create and sustain demand for their cloud services. By investing in data centers & workforce training, the large tech platforms of Amazon, Google, and Microsoft draw developers, companies, and governments to their tools & services.

Artificial intelligence models have become increasingly adept at generating computer code. They are powerful and promising tools for software development across many industries, but they can also pose direct and indirect cybersecurity risks. This report identifies three broad categories of risk associated with AI code generation models and discusses their policy and cybersecurity implications.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This three-part series uses data from a variety of sources to track how three cloud providers—Amazon, Alphabet, and Microsoft—distribute their financial resources to create and sustain demand for their cloud services. By investing in data centers & workforce training, the large tech platforms of Amazon, Google, and Microsoft draw developers, companies, and governments to their tools & services.

Computer scientists have long sought to build systems that can actively and autonomously carry out complicated goals in the real world—commonly referred to as artificial intelligence "agents." Recently, significant progress in large language models has fueled new optimism about the prospect of building sophisticated AI agents. This CSET-led workshop report synthesizes findings from a May 2024 workshop on this topic, including what constitutes an AI agent, how the technology is improving, what risks agents exacerbate, and intervention points that could help.

As critical infrastructure operators and providers seek to harness the benefits of new artificial intelligence capabilities, they must also manage associated risks from both AI-enabled cyber threats and potential vulnerabilities in deployed AI systems. In June 2024, CSET led a workshop to assess these issues. This report synthesizes our findings, drawing on lessons from cybersecurity and insights from critical infrastructure sectors to identify challenges and potential risk mitigations associated with AI adoption.

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.

President Biden's October 2023 executive order prioritizes the governance of artificial intelligence in the federal government, prompting the urgent creation of AI risk management standards and procurement guidelines. Soon after the order's signing, the Office of Management and Budget issued guidance for federal departments and agencies, including minimum risk standards for AI in federal contracts. Similar to cybersecurity, procurement rules will be used to enforce AI development best practices for federal suppliers. This report offers recommendations for implementing AI risk management procurement rules.

Research participants who read propaganda generated by GPT-3 davinci (a large language model) were nearly as persuaded as those who read real propaganda from Iran or Russia, according to a new peer-reviewed study by Josh A. Goldstein and co-authors.

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.