Priorities and principles for effective third party assessments
OpenAI published "Priorities and principles for effective third party assessments" — it sets out four priority areas for third-party assessment (evaluating safety cases that span training, evaluation, and internal and external deployment; evaluating core safeguards, including adversarial testing and monitoring; preparedness capability evaluations, such as chemical, biological and cyber, along with alignment evaluations; and investigating alignment-failure incidents) and seven principles (clearly specified, agreed assessment targets; proportionate access; transparency of methods and criteria; expertise, independence and disclosure of conflicts of interest; security and confidentiality; a guaranteed remediation period before publication; and responsible disclosure). As examples of access it has provided in the past, it cites technical safeguard information, viewing of chains of thought, and non-public deployment data
Why it matters
A document that came after Anthropic's embedded evaluation (resident evaluators) last week, and part of a trend in which frontier labs have each begun to codify "how far to let external evaluation in". However, there are no quantitative commitments on the number of evaluators or the level of access, and the "remediation period before publication" is a clause that can restrict evaluators' discretion to publish, so real independence will be decided contract by contract
Source — Primary sources first; secondary reports are labelled “Press”
Officialopenai.comhttps://openai.com/index/priorities-principles-third-party-assessments/Added in Week 40, 2026 · See that week