Open Problems in Technical AI Governance
Authors: Anka Reuel, Benjamin Bucknall, Stephen Casper, Timothy Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, David Bau, Paul Bricman, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel Kochenderfer, Robert Trager
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we explain what technical AI governance is, outline why it is important, and present a taxonomy and incomplete catalog of its open problems. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance. |
| Researcher Affiliation | Collaboration | 1Stanford University 2University of Oxford 3Oxford Martin AI Governance Initiative 4MIT CSAIL 5Institute for Progress 6Center for a New American Security 7interface Tech Analysis and Policy Ideas for Europe e.V. 8Institute for AI Policy and Strategy 9Cooperative AI Foundation 10Centre for the Governance of AI 11Mila 12Open Mined 13Independent Researcher 14Cohere for AI 15Hugging Face 16University of Cambridge 17The Future Society 18Palisade Research 19Northeastern University 20Noema Research 21University of California, Berkeley 22University of Montreal 23MIT 24Stanford HAI 25Virtue AI |
| Pseudocode | No | The paper is a broad overview of open problems in technical AI governance, outlining a taxonomy, motivations, and research questions. It does not present structured pseudocode or algorithm blocks for a specific method or procedure. |
| Open Source Code | No | The paper presents a broad overview and taxonomy of open problems in technical AI governance and does not describe a specific methodology that would involve the release of its own source code. |
| Open Datasets | No | The paper discusses various types of data and datasets (e.g., LAION-5B, Fine Web) as part of existing research or open problems in technical AI governance. However, the paper itself is a survey and taxonomy and does not present experiments that utilize specific datasets for which access information would be provided by the authors. |
| Dataset Splits | No | The paper is a survey and taxonomy of open problems in technical AI governance and does not involve empirical experiments requiring dataset splits for reproduction. |
| Hardware Specification | No | The paper is a survey and taxonomy of open problems in technical AI governance and does not report on experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is a survey and taxonomy of open problems in technical AI governance and does not describe a specific methodology that requires listing software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper describes its methodology for developing a taxonomy through literature review and expert consultation (Appendix B) rather than detailing an experimental setup with hyperparameters or specific training configurations. |