Preparedness Framework
webCredibility Rating
High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: OpenAI
OpenAI's official framework for catastrophic risk evaluation of frontier models; a key industry document comparable to Anthropic's Responsible Scaling Policy, often cited in discussions of voluntary AI safety commitments and responsible scaling.
Metadata
Summary
OpenAI's Preparedness Framework outlines a structured approach to evaluating and mitigating catastrophic risks from frontier AI models before and after deployment. It establishes a 'Scorecard' system for tracking risk levels across threat categories including CBRN, cybersecurity, persuasion, and model autonomy, with defined safety thresholds that determine deployment decisions. The framework also introduces a Preparedness team responsible for continuous risk assessment and red-teaming of frontier models.
Key Points
- •Defines four risk categories for evaluation: CBRN (chemical, biological, radiological, nuclear), cybersecurity, persuasion/influence operations, and model autonomy.
- •Introduces a 'Scorecard' with risk levels (low, medium, high, critical) where models rated 'critical' in any category cannot be deployed.
- •Establishes ongoing pre- and post-deployment evaluations by a dedicated Preparedness team with red-teaming and forecasting responsibilities.
- •Creates governance mechanisms including a Safety Advisory Group and requires executive or board-level sign-off for deploying high-risk models.
- •Represents OpenAI's formalization of responsible scaling commitments, similar in spirit to Anthropic's RSP.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Safety Cases | Approach | 91.0 |
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