Towards safety cases for frontier AI training

OpenAI proposes that structured safety documentation should be required before continuing any frontier reinforcement learning training run, aspiring to reach the level of comprehensive, evidence-based "safety cases" used in other safety-critical industries like aviation and nuclear power. The article outlines initial guidelines across three areas: (1) Technical safeguards covering model alignment (training environment and grading quality, alignment measurement), containment via layered infrastructure security and red-teaming, and real-time monitoring systems; (2) Operational guidelines including dissents/pre-mortems, senior leadership approvals with veto power, accountability structures, pausing runbooks, internal transparency, audits, escalation processes, technical controls that "fail closed," rollback ability, and residual risk enumeration; and (3) best practices for investigating severe misalignment incidents, including internal transparency during investigations, root-cause analysis of training dynamics, operational postmortems, development of regression tests, and public disclosure of findings. These guidelines represent OpenAI's current recommendations, acknowledged to be evolving, and are shared to invite community feedback.

(Source：OpenAI)