The UNIDIR project on Confidence-Building Measures (CBMs) for Artificial Intelligence (AI) aimed to advance conversations about the objectives, format and ways forward for AI-focused CBMs at the multilateral level.
The project consisted of two distinct phases:
- The first developed a comprehensive taxonomy of AI risks in the context of international security.
- The second provided a theoretical and historical overview of CBMs, probing for viable ways to move forward and seeking input from States.
This report concludes the second phase of the project and presents a framework for conceptual and practical considerations for CBMs for AI, drawing on lessons learned from other domains and from perspectives shared by a diverse group of States.
This study and the consultation UNIDIR convened with national representatives, which included a workshop and surveys, brought to light some key areas of agreement and shared concerns. As conversations about future CBMs begin to take shape, this publication provides a realistic assessment of current priorities and invites reflection on next steps.
The research output from the first phase of the project is available in the form of our earlier publication AI and International Security: Understanding the Risks and Paving the Path for Confidence-Building Measures.
The initial framing paper which launched the project is also available as Confidence-Building Measures for Artificial Intelligence: A Framing Paper.
Citation: Ioana Puscas, Confidence-Building Measures for Artificial Intelligence. A Multilateral Perspective, UNIDIR, Geneva, 2024.