Open Problems in Frontier AI Risk Management
Marta Ziosi, Miro Plueckebaum, Stephen Casper, Henry Papadatos, Ze Shen Chin, Peter Slattery, James Gealy, Tim G. J. Rudner, Brian Tse, Ariel Gil, Patricia Paskov, Maximilian Negele, Rokas Gipi\v{s}kis, Nada Madkour, Vera Lummis, Rupal Jain, Luise Eder, Kristina Fort, Malou C. van Draanen Glismann, In\`es Belhadj, Amin Oueslati, Anna K. Wisakanto, Richard Mallah, Koen Holtman, Ranj Zuhdi, Daniel S. Schiff, Jessica Newman, Malcolm Murray, Robert Trager
Why It Matters
What makes this one worth your time
Understanding these open problems is crucial for researchers and practitioners to effectively address the evolving risks associated with frontier AI technologies.
This work systematically maps unresolved challenges in frontier AI risk management.
Summary
The paper identifies and categorizes open problems in frontier AI risk management, examining the risk management process and the actors involved without proposing specific solutions.
Key contributions
- Systematic identification of open problems in frontier AI risk management.
- Classification of these problems based on their nature and the responses they require.
- Mapping of relevant actors who can address these challenges.
Notable insights
- The classification of open problems into categories based on consensus and alignment highlights the complexity of risk management in rapidly evolving AI contexts.
- The structured review of the literature across all stages of risk management provides a comprehensive overview that may guide future research directions.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2604.25982v1 Announce Type: cross Abstract: Frontier AI both amplifies existing risks and introduces qualitatively novel challenges. Not only is there a notable lack of stable scientific consensus resulting from the rapid pace of technological change, but emerging frontier AI safety practices are often misaligned with, or may undermine, established risk management frameworks. To address these challenges, we systematically surface open problems in frontier AI risk management. Adopting a problem-oriented approach, we examine each stage of the risk management process - risk planning, identification, analysis, evaluation, and mitigation - through a structured review of the literature, identifying unresolved challenges and the actors best positioned to address them. Recognising that different types of open problems call for different responses, we classify open problems according to whether they reflect (a) a lack of scientific or technical consensus, (b) misalignment with, or challenges to, established risk management frameworks, or (c) shortcomings in implementation despite apparent consensus and alignment. By mapping these open problems and identifying the actors best positioned to address them - including developers, deployers, regulators, standards bodies, researchers, and third-party evaluators - this work aims to clarify where progress is needed to enable robust and meaningful consensus on frontier AI risk management.The paper does not propose specific solutions; instead, it provides a problem-oriented, agenda-setting reference document, complemented by a living online repository, intended to support coordination, reduce duplication, and guide future research and governance efforts.