6 citations · 12 across the 9 of their papers we have counts for
9 papers
An Example Safety Case for Safeguards Against Misuse
Joshua Clymer, Jonah Weinbaum, Robert Kirk +3
Existing evaluations of AI misuse safeguards provide a patchwork of evidence that is often difficult to connect to real-world decisions. To bridge this gap, we describe an end-to-e…
Bare Minimum Mitigations for Autonomous AI Development
Joshua Clymer, Isabella Duan, Chris Cundy +10
Artificial intelligence (AI) is advancing rapidly, with the potential for significantly automating AI research and development itself in the near future. In 2024, international sci…
A sketch of an AI control safety case
Tomek Korbak, Joshua Clymer, Benjamin Hilton +2
As LLM agents gain a greater capacity to cause harm, AI developers might increasingly rely on control measures such as monitoring to justify that they are safe. We sketch how devel…
Towards evaluations-based safety cases for AI scheming
Mikita Balesni, Marius Hobbhahn, David Lindner +13
We sketch how developers of frontier AI systems could construct a structured rationale -- a 'safety case' -- that an AI system is unlikely to cause catastrophic outcomes through sc…
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
Hjalmar Wijk, Tao Lin, Joel Becker +20
Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations fo…
Affirmative safety: An approach to risk management for high-risk AI
Akash R. Wasil, Joshua Clymer, David Krueger +3
Prominent AI experts have suggested that companies developing high-risk AI systems should be required to show that such systems are safe before they can be developed or deployed. T…
Poser: Unmasking Alignment Faking LLMs by Manipulating Their Internals
Joshua Clymer, Caden Juang, Severin Field
Like a criminal under investigation, Large Language Models (LLMs) might pretend to be aligned while evaluated and misbehave when they have a good opportunity. Can current interpret…
Safety Cases: How to Justify the Safety of Advanced AI Systems
Joshua Clymer, Nick Gabrieli, David Krueger +1
As AI systems become more advanced, companies and regulators will make difficult decisions about whether it is safe to train and deploy them. To prepare for these decisions, we inv…
Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure Domains
Joshua Clymer, Garrett Baker, Rohan Subramani +1
As AI systems become more intelligent and their behavior becomes more challenging to assess, they may learn to game the flaws of human feedback instead of genuinely striving to fol…