How an assessor could investigate alignment of unreleased AI models

AI developers and the public want to know whether AI agents would pursue very harmful actions, like unsanctioned cyberattacks. An alignment assessment would attempt to understand the worst misaligned actions a developer’s agents might pursue in plausible future situations. This would involve: A review of serious misaligned behaviors from all near-frontier internal models observed across training, evaluation, and internal use. An assessment of reasons why the observed behaviors might not be representative of the worst misaligned actions the developer’s agents would take in plausible future situations. This would include: An assessment of whether alignment evals and counterfactual simulations indicate the developer’s agents might take much worse actions under plausible alternative circumstances (although evaluation awareness will likely limit how much the assessor can rely on this evidence). An assessment of whether the developer’s training process might incentivize agents to game evaluations or otherwise deliberately hide misalignment, or might otherwise be overfit to the assessor’s detection methods. An assessment of whether alignment evals and counterfactual simulations indicate the developer’s agents might take much worse actions under plausible alternative circumstances (although evaluation awareness will likely limit how much the assessor can rely on this evidence). An assessment of whether the developer’s training process…