OpenAI's Preparedness Framework (Beta) outlines a set of procedures for developing and deploying their frontier AI models safely. It aims to mitigate potential risks associated with increasingly powerful AI, particularly catastrophic risks with significant societal consequences.
- Safety Evaluations and Scorecards: Regular evaluations assess model capabilities and risks, generating "scorecards" that track pre-mitigation and post-mitigation risk levels across various categories like cybersecurity, persuasion, and model autonomy.
- Risk Thresholds: Specific risk thresholds for each category determine whether models can be deployed or developed further. Only models with "medium" or lower post-mitigation scores can be deployed, and only those with "high" or lower scores can be further developed.
- Dedicated Team and Structure: A dedicated Preparedness team oversees technical work, conducting evaluations, analyzing model limits, and preparing reports. A cross-functional Safety Advisory Group reviews these reports and provides recommendations to leadership and the Board of Directors, who ultimately make model development and deployment decisions.
- Safety Protocols and Accountability: Regular safety drills simulate potential issues, and rapid response protocols address urgent concerns. External audits and feedback from independent third parties are encouraged. OpenAI also welcomes red-teaming and external evaluations of its models.
- Addressing Unknown Risks: Collaboration with internal and external teams helps track real-world misuse and emergent misalignment risks. Research focuses on measuring how risks evolve as models scale, and a continuous process identifies potential "unknown unknowns."
The framework is currently in Beta and considered a living document, subject to ongoing updates based on new learning and feedback. OpenAI welcomes public input and encourages discussion on safety concerns and potential improvements.
The Preparedness Framework represents OpenAI's commitment to responsible AI development and deployment, acknowledging the potential risks associated with powerful AI models and outlining a proactive approach to mitigating them. While still under development, the framework serves as a valuable starting point for ongoing efforts to ensure AI technology's safe and beneficial advancement.
- Refine Risk Categorization and Metrics: OpenAI should consider revising the current risk categories (cybersecurity, CBRN, persuasion, autonomy) to encompass a wider range of potential concerns and further refine the metrics used to evaluate and quantify these risks. This could involve including factors like societal disruption, economic instability, and environmental impact.
- Strengthen Interdependence Assessment: Currently, the framework focuses on individual model risks. OpenAi should incorporate methods to assess potential synergies and cascading effects when deploying multiple AI models concurrently. This could involve simulations and scenario planning to identify potential unforeseen interactions and emergent risks.
- Expand Human-AI Collaboration Mechanisms: OpenAI should explore additional ways to ensure humans remain meaningfully involved in decision-making processes throughout the AI lifecycle. This could include integrating human feedback loops into AI model development and deployment and developing frameworks for human override of autonomous AI systems in critical situations.
- Prioritize Public Trust and Communication: Develop a comprehensive communication strategy to engage the public and build trust in OpenAI's safety efforts. This could involve regularly publishing reports on risks identified and mitigation strategies employed and hosting open forums for public dialogue and feedback.
- Foster International Collaboration and Standards: The company should collaborate with other research institutions, governments, and international organizations to establish global standards and best practices for safe AI development and deployment. This could involve sharing insights and expertise, harmonizing risk assessment methodologies, and developing joint research initiatives.
