Shared crawled corpus for the Responsible AI governance source target. Use this Space as a grounding library, not as a generic writing persona.

Source target: web_ai_governance
Ranking guidance: Prioritize official regulator guidance, standards, framework documents, consultation materials, and enforcement/regulatory updates. Capture jurisdiction, version, status, scope, and obligations versus recommendations.

Answer behavior: lead with the clearest source-backed finding, preserve source provenance, surface dates and trust signals, state uncertainty when evidence is incomplete, and avoid unsupported synthesis.

Responsible AI materials from standards, risk frameworks, regulator guidance, consultations, and legal sources.

AI Governance Library

EU AI Act mapping: evidence status
I found the attached EU AI Act page at the European Commission digital strategy site, but the available search pass returned no extractable evidence from that source, so I cannot responsibly provide a substantive jurisdiction-aware mapping from the material on hand.
Attached EU AI Act page
As a result, the risk-category obligations below are **unverified placeholders** rather than confirmed statements from the attached material, and the timing notes are likewis

Q1. A project team is building an AI system inventory. Which item belongs in the inventory according to the NIST sources?
- A list of the system's artifacts, such as incident response plans, data dictionaries, source code links, and AI actor contact information
- A marketing summary of the model's business value
- A weekly calendar of team meetings
- A list of unrelated office software licenses
Answer: A list of the system's artifacts, such as incident response plans, data dictionaries, source c

In July 2024, NIST drew a line between AI governance topics ready for standardization and areas needing more foundational work. It identified risk-based management, security, privacy, provenance and incident response among topics with strong foundations.

But interpretability, explainability and human-AI configuration were not yet ready for standardization. This distinction is about readiness for standards, not a claim that every area on the ready-now list is fully settled.

Risk-based AI governance is ready for immediate standardization.
Security, privacy, transparency, incident response, recovery, training-data practices, terminology, and taxonomy are near-term standards candidates.
TEVV procedures can be standardized now, but TEVV metrics and scientific validity still need development.
Human oversight alone is not enough; NIST points to unresolved measurement and configuration gaps, so standards should also require accountability, documentation, feedback, and ind