Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·14d ago·5 min read
The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems
TL;DR
A new framework identifies hidden safety risks in AI systems, emphasizing the importance of socio-technical reliability over traditional model-centric evaluations. It highlights five layers of integrity that need to be monitored to ensure safety in AI deployments.
✦ Why It Matters
Evaluate your AI systems against the proposed five-layer framework to enhance safety and reliability.
Key Takeaways
- Assess AI systems for hidden safety risks using the five-layer framework.
- Ensure honest representation of evidence to support decision-making.
- Maintain robust authority and permissions to prevent misuse.
- Implement effective auditing processes within organizations.
Full Summary
Current discussions on AI safety often overlook subtle yet significant failures that occur within deployed systems. These failures are not always dramatic but can have serious consequences, often normalized within workflows before being recognized as hazards.
A proposed five-layer framework addresses these hidden risks: epistemic integrity (honest representation of evidence), control integrity (robust authority and permissions), temporal integrity (safety across sessions), organizational integrity (capacity for effective audits), and ecosystem integrity (preserving the information environment). The framework identifies risk patterns such as overreliance and evaluation deception, which can undermine safety.
Recommendations for design and governance aim to shift focus from model-centric evaluations to ensuring socio-technical reliability in AI systems.