The paper "Open Problems in Technical AI Governance" by Anka Reuel, Ben Bucknall, and others, explores the challenges and opportunities in technical AI governance (TAIG). TAIG refers to the technical analysis and tools that support effective governance of AI systems. The authors define TAIG as a field that helps identify areas needing intervention, assess potential governance actions, and enhance governance options through enforcement, incentivization, or compliance mechanisms. The paper highlights the importance of TAIG in addressing the growing risks and opportunities presented by AI. It outlines a taxonomy of TAIG, structured around two dimensions: capacities and targets. Capacities include actions such as access and verification, while targets encompass key elements of the AI value chain, such as data and models. The authors present a taxonomy of open problems within each category, providing concrete research questions for future work. Key contributions of the paper include: 1. Introducing the emerging field of TAIG and its significance. 2. Presenting a taxonomy of TAIG capacities and targets. 3. Outlining open problems in each category, along with specific research questions. The paper emphasizes the need for technical solutions to address the complex and often normative social issues surrounding AI governance. It also acknowledges the potential pitfalls of relying solely on technical fixes and the importance of balancing capacities that may be in tension with each other. The authors argue that progress in technical problems can help ensure more robust AI governance. The paper is structured into several sections, each focusing on different aspects of TAIG, including assessment, data, compute, models and algorithms, and deployment. Each section addresses specific challenges and provides examples of research questions to guide future work.The paper "Open Problems in Technical AI Governance" by Anka Reuel, Ben Bucknall, and others, explores the challenges and opportunities in technical AI governance (TAIG). TAIG refers to the technical analysis and tools that support effective governance of AI systems. The authors define TAIG as a field that helps identify areas needing intervention, assess potential governance actions, and enhance governance options through enforcement, incentivization, or compliance mechanisms. The paper highlights the importance of TAIG in addressing the growing risks and opportunities presented by AI. It outlines a taxonomy of TAIG, structured around two dimensions: capacities and targets. Capacities include actions such as access and verification, while targets encompass key elements of the AI value chain, such as data and models. The authors present a taxonomy of open problems within each category, providing concrete research questions for future work. Key contributions of the paper include: 1. Introducing the emerging field of TAIG and its significance. 2. Presenting a taxonomy of TAIG capacities and targets. 3. Outlining open problems in each category, along with specific research questions. The paper emphasizes the need for technical solutions to address the complex and often normative social issues surrounding AI governance. It also acknowledges the potential pitfalls of relying solely on technical fixes and the importance of balancing capacities that may be in tension with each other. The authors argue that progress in technical problems can help ensure more robust AI governance. The paper is structured into several sections, each focusing on different aspects of TAIG, including assessment, data, compute, models and algorithms, and deployment. Each section addresses specific challenges and provides examples of research questions to guide future work.