KB article
Governance for AI Analytics: Change Control for Semantics
Governance ensures semantic changes are intentional and traceable.
arf-kbai-readiness-interoperabilitysemantic-driftsemantic-contractmeasure-inventory
TL;DR
- Semantic changes must be reviewed.
- Governance prevents drift and surprises.
The problem
- Metrics change without documentation or approval.
- AI outputs drift over time.
Why it matters
- Governance maintains trust and compliance.
- It protects downstream consumers.
Symptoms
- KPIs change unexpectedly after model updates.
- Stakeholders lose confidence.
Root causes
- No change control for semantic updates.
- Lack of ownership for key metrics.
What good looks like
- Change review process for metrics.
- Owners accountable for definitions.
How to fix
- Implement a semantic change review process.
- Track versions and communicate updates.
- Automate tests for key metrics.
Pitfalls
- Governance too heavy to use.
- Changes made outside the process.
Checklist
- Metric owners assigned.
- Change reviews implemented.
- Version history maintained.
Framework placement
Primary ARF layer: AI Readiness & Interoperability. Diagnostic bridge: data-movement-reliability, semantic-reliability, execution-reliability, change-reliability.