Director of Data Science and Research Catherine Aiken outlines how she tested frameworks for her report "Classifying AI Systems" in partnership with OECD.AI

A CSET report found that China's People's Liberation Army spends at least $1.6 billion each year on AI-related systems and equipment.

In an opinion piece for Foreign Affairs, CyberAI Director John Bansemer unpacks how AI can be leveraged for cyberattacks, while also bolstering cyber defense.

Findings from CSET's report "AI and the Future of Disinformation Campaigns, Part 1: The RICHDATA Framework" offers insight into the use of AI and machine learning to amplify disinformation campaigns.

Artificial intelligence offers enormous promise to advance progress, and powerful capabilities to disrupt it. This policy brief is the first installment of a series that examines how advances in AI could be exploited to enhance operations that automate disinformation. Introducing the RICHDATA framework—a disinformation kill chain—this report describes the stages and techniques used by human operators to build disinformation campaigns.

As artificial intelligence transforms the economy and American society, it will also transform the practice of law and the role of courts in regulating its use. What role should, will, or might judges play in addressing the use of AI? And relatedly, how will AI and machine learning impact judicial practice in federal and state courts? This report is intended to provide a framework for judges to address AI.

A new CSET report by Tim Rudner and Helen Toner introduces specification in machine learning as a key element in greater AI safety.

By combining a versatile and frequently updated bibliometrics tool — the CSET Map of Science — with more hands-on analyses of technical developments, this brief outlines a methodology for measuring the publication growth of AI-related topics, where that growth is occurring, what organizations and individuals are involved, and when technical improvements in performance occur.

This paper is the fourth 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. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.

CSET's Husanjot Chahal discusses India's economic role as an emerging AI superpower.