Artificial intelligence is reshaping military decision-making. This concise overview explores how AI-enabled systems can enhance situational awareness and accelerate critical operational decisions—even in high-pressure, dynamic environments. Yet, it also highlights the essential need for clear operational scopes, robust training, and vigilant risk mitigation to counter the inherent challenges of using AI, such as data biases and automation pitfalls. This report offers a balanced framework to help military leaders integrate AI responsibly and effectively.

As new advanced AI systems roll out, there is widespread disagreement about malicious use risks. Are bad actors likely to misuse these tools for harm? This report presents a simple framework to guide the questions researchers ask—and the tools they use—to evaluate the likelihood of malicious use.

In response to the Office of Science and Technology Policy's request for input on an AI Action Plan, CSET provides key recommendations for advancing AI research, ensuring U.S. competitiveness, and maximizing benefits while mitigating risks. Our response highlights policies to strengthen the AI workforce, secure technology from illicit transfers, and foster an open and competitive AI ecosystem.

Explainability and interpretability are often cited as key characteristics of trustworthy AI systems, but it is unclear how they are evaluated in practice. This report examines how researchers evaluate their explainability and interpretability claims in the context of AI-enabled recommendation systems and offers considerations for policymakers seeking to support AI evaluations.

This follow-up report builds on the foundational framework presented in the March 2024 CSET issue brief, “An Argument for Hybrid AI Incident Reporting,” by identifying key components of AI incidents that should be documented within a mandatory reporting regime. Designed to complement and operationalize our original framework, this report promotes the implementation of such a regime. By providing guidance on these critical elements, the report fosters consistent and comprehensive incident reporting, advancing efforts to document and address AI-related harms.

The U.S. Army’s 18th Airborne Corps can now target artillery just as efficiently as the best unit in recent American history—and it can do so with two thousand fewer servicemembers. This report presents a case study of how the 18th Airborne partnered with tech companies to develop, prototype, and operationalize software and artificial intelligence for clear military advantage. The lessons learned form recommendations to the U.S. Department of Defense as it pushes to further develop and adopt AI and other new technologies.

CSET submitted the following comment in response to a Request for Information (RFI) from the Department of Commerce regarding 89 FR 27411.

How to govern artificial intelligence is a concern that is rightfully top of mind for lawmakers and policymakers.To govern AI effectively, regulators must 1) know the terrain of AI risk and harm by tracking incidents and collecting data; 2) develop their own AI literacy and build better public understanding of the benefits and risks; and 3) preserve adaptability and agility by developing policies that can be updated as AI evolves.

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.”