Section 166 · Chapter 20, The Practical Playbook
Governance for AI Quality
AI quality needs ownership, decision rights, audit trails, and escalation paths before the incident happens.
escalationcompliancegovernance quality
What to do
- Define logging and retention.
- Define runnable checks that exercise escalation, compliance, and governance quality.
- Set acceptable outcomes and blocker failures for escalation, compliance, and governance quality before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for escalation, compliance, governance quality needed to reproduce work on Governance for AI Quality.
- Report results for escalation, compliance, governance quality by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
The deeper move is that governance connects eval provenance, incident response, access control, vendor management, and release gates. The audit trail should show who approved what evidence under which constraints.
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Cite this page
Jason Arbon. "Governance for AI Quality." Testing AI Knowledge Edition, section 166.
https://jarbon.ai/testing-ai/knowledge/ch166-governance-quality.html