Priorities and principles for effective third party assessments

Frontier AI labs carry an immense responsibility in training, evaluating, and deploying models safely. Third party assessments are a critical part of balancing that responsibility, expanding opportunities for input on AI safety, keeping the world informed, and keeping labs accountable to clear and independently supported safety claims.

As part of our efforts to pace the frontier, OpenAI is committed to supporting independent assessments with deep levels of access across training, evaluation, and deployment. That access should enable assessors to challenge our assumptions, identify risks we may have missed, and reach their own conclusions about the effectiveness of our safeguards.

We have long worked with third party assessors at various stages of the model development and deployment process. We have also incorporated third party assessments into our Preparedness Framework practices and supported organizations and legislation that advocate for a more rigorous and accountable process. Throughout these engagements, we have provided deep forms of access, including information about our technical safeguards, visible chain of thought access, and unprecedented levels of confidential data and internal deployment access for incident response and monitor red teaming. The priorities and principles shared here focus on our engagement with independent assessment organizations in the private and non-profit sector on technical safety assessments. They complement our work with governments on testing and evaluation, where distinct roles and responsibilities may call for different approaches.

Priority areas for assessment

Third party assessments are most useful when they address specific, consequential questions: Does the evidence support a lab’s safety case and safety claims? Do evaluations adequately test the risks they are intended to measure? Do safeguards work under realistic conditions?