Towards safety cases for frontier AI training
OpenAI sets out initial guidelines for "safety cases" that should be documented before any frontier reinforcement learning training run. The safety case covers three layers: alignment training (RL environment and grader reviews, alignment evals, not training on chain-of-thought), containment (sandbox hardening, red-teaming, immutable transcripts) and live monitoring with rapid response. It also lists operational practices (dissents, senior-leader veto approvals, accountability, fail-closed technical controls, auditors, escalations, rollback) and practices for investigating severe misalignment incidents. The guidance matters because it is a concrete, lab-published template for governing frontier training runs.
Organisations named here
- OpenAI
- National Transportation Safety Board
- NTSB
Techniques named here
- Safety cases for frontier AI training
- Automated dataset reviews
- Grader tuning
- Prior run analysis
- Backtesting alignment evaluations
- Tracking evaluation gaming
- Worst-case stress tests
- Not training on chain-of-thought
- Containment red-teaming
- Immutable transcripts
- Rapid response alerting
- Dissents
- Misalignment root-cause analysis
- Incident-derived regression evals
Related in ai-safety

