Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems
arXiv:2605.23297v1 Announce Type: new Abstract: AI-enabled services deployed in critical digital infrastructure are subject to governance obligations spanning transparency, accountability, fairness, and traceability. Compliance today remains documentation-centric: obligations are described in prose, audits rely on static checklists, and verification depends on manual review. Such approaches do not scale to automated AI systems. This paper introduces Ontological Knowledge Blocks (OKBs), a programmable governance infrastructure that compiles regulatory obligations into machine-checkable constrai
The proliferation of AI systems in critical infrastructure necessitates a more robust and scalable approach to compliance than current manual methods, driving the need for automated solutions like OKBs.
This development proposes a foundational architectural layer for ensuring AI governance, transforming regulatory compliance from a manual burden into an executable, verifiable process, which is critical for trustworthy AI adoption.
Compliance for AI systems shifts from documentation-centric, human-reviewed processes to programmable, machine-checkable constraints, enabling automated validation and audit.
- · AI governance solution providers
- · Organizations deploying AI in critical infrastructure
- · AI developers focused on explainability and compliance tools
- · Traditional AI auditing firms reliant on manual processes
- · Organizations with opaque AI development practices
Automated verification of AI regulatory compliance becomes feasible, reducing the cost and complexity of AI deployment in regulated sectors.
Increased trust and accelerated adoption of AI in sensitive applications and critical infrastructure due to verifiable adherence to ethical and legal standards.
The development of a global standard for machine-readable AI governance, potentially leading to interoperable compliance frameworks across jurisdictions.
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Read at arXiv cs.AI