This paper is the first 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. In it, the authors introduce three categories of AI safety issues: problems of robustness, assurance, and specification. Other papers in this series elaborate on these and further key concepts.

The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.

CSET Founding Director Jason Matheny testified before the House Budget Committee for its hearing, "Machines, Artificial Intelligence, & the Workforce: Recovering and Readying Our Economy for the Future." Dr. Matheny's full testimony as prepared for delivery can be found below.

The rise of deepfakes could enhance the effectiveness of disinformation efforts by states, political parties and adversarial actors. How rapidly is this technology advancing, and who in reality might adopt it for malicious ends? This report offers a comprehensive deepfake threat assessment grounded in the latest machine learning research on generative models.

See our translation of China’s plan for science and technology innovation during the years of the 13th Five-Year Plan (2016-2020). The plan puts forward plan details specific technologies identified as near-term priorities for research and investment.