Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 25·6 min read
Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems
TL;DR
AI systems in critical infrastructure must comply with regulations on transparency and fairness, but current audits rely on manual documentation review that doesn't scale. Ontological Knowledge Blocks (OKBs) automate compliance by converting regulatory rules into machine-checkable constraints using semantic web standards (RDF/OWL and SHACL validation).
✦ Why It Matters
Engineers can automate compliance checking without modifying production code by adopting profile-based governance frameworks.
Key Takeaways
- Adopt Ontological Knowledge Blocks for scalable AI compliance.
- Utilize profile-based governance for dynamic regulatory adjustments.
- Implement SHACL validation rules for automated checks.
- Leverage open-source prototypes for practical applications.
How It Works
Ontological Knowledge Blocks (OKBs) are structured as a 5-tuple that connects regulatory obligations to a concept schema, validation rules, evidence requirements, and provenance links. This structure allows for the creation of machine-checkable constraints that can be validated automatically.
A deterministic regulatory compiler processes structured records into knowledge base modules, enabling the reconfiguration of governance profiles without modifying the underlying service code.