Towards embedded evaluations for scheming propensities
Addresses core AI safety research questions about detecting and preventing deceptive alignment in advanced AI systems.
AI Summary
Researchers propose four key safety claims needed to substantiate that AI models lack scheming propensities, emphasizing embedded evaluators with persistent deep access.
Excerpt
TL;DR: A safety case for scheming must be able to substantiate these four key claims: scheming was not incentivized during training, strong evaluations and red teaming suggest that the model does not have a propensity to scheme, scheming was never attempted during internal deployment, and scheming reasoning would be detected (e.g., via CoT monitoring). All four of these claims should be addressed by embedded evaluators with persistent, deep access. Introduction A scheming AI is one that covertly