8 citations · 20 across the 4 of their papers we have counts for
6 papers · 1 filter
Safety cases for frontier AI
Marie Davidsen Buhl, Gaurav Sett, Leonie Koessler +2
As frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do s…
From Principles to Rules: A Regulatory Approach for Frontier AI
Jonas Schuett, Markus Anderljung, Alexis Carlier +2
Several jurisdictions are starting to regulate frontier artificial intelligence (AI) systems, i.e. general-purpose AI systems that match or exceed the capabilities present in the m…
Risk thresholds for frontier AI
Leonie Koessler, Jonas Schuett, Markus Anderljung
Frontier artificial intelligence (AI) systems could pose increasing risks to public safety and security. But what level of risk is acceptable? One increasingly popular approach is…
Training Compute Thresholds: Features and Functions in AI Regulation
Lennart Heim, Leonie Koessler
Regulators in the US and EU are using thresholds based on training compute--the number of computational operations used in training--to identify general-purpose artificial intellig…
Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives
Elizabeth Seger, Noemi Dreksler, Richard Moulange +19
Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI mode…
Risk assessment at AGI companies: A review of popular risk assessment techniques from other safety-critical industries
Leonie Koessler, Jonas Schuett
Companies like OpenAI, Google DeepMind, and Anthropic have the stated goal of building artificial general intelligence (AGI) - AI systems that perform as well as or better than hum…