Policy and research communities strive to mitigate AI harm while maximizing its benefits. Achieving effective and trustworthy AI necessitates the establishment of a shared language. The analysis of policies across different countries and research literature identifies consensus on six critical concepts: accountability, explainability, fairness, privacy, security, and transparency.

This paper is the fifth 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. This paper explores the opportunities and challenges of building AI systems that “know what they don’t know.”

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.

Artificial Intelligence incidents have been occurring with the rapid advancement of AI capabilities over the past decade. However, there is not yet a concerted policy effort in the United States to monitor, document, and aggregate AI incident data to enhance the understanding of AI-related harm and inform safety policies. This report proposes a federated approach consisting of hybrid incident reporting frameworks to standardize reporting practices and prevent missing data.

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.

On February 2, 2024, CSET's Assessment and CyberAI teams submitted a response to NIST's Request for Information related to the Executive Order Concerning Artificial Intelligence (88 FR 88368). In the submission, CSET compiles recommendations from six CSET reports and analyses in order to assist NIST in its implementation of AI Executive Order requirements.

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.

Strengthening the federal cyber workforce is one of the main priorities of the National Cyber Workforce and Education Strategy. The National Science Foundation’s CyberCorps Scholarship-for-Service program is a direct cyber talent pipeline into the federal workforce. As the program tries to satisfy increasing needs for cyber talent, it is apparent that some form of program expansion is needed. This policy brief summarizes trends from participating institutions to understand how the program might expand and illustrates a potential future artificial intelligence (AI) federal scholarship-for-service program.

While recent progress in artificial intelligence (AI) has relied primarily on increasing the size and scale of the models and computing budgets for training, we ask if those trends will continue. Financial incentives are against scaling, and there can be diminishing returns to further investment. These effects may already be slowing growth among the very largest models. Future progress in AI may rely more on ideas for shrinking models and inventive use of existing models than on simply increasing investment in compute resources.