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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
AI Governance Framework Components
| Component | Description | Key Activities |
|---|---|---|
| AI risk assessment | Identify and assess risks before deployment | Bias testing; impact assessment; risk classification |
| Model governance | Manage AI models throughout their lifecycle | Model registry; version control; performance monitoring |
| Data governance for AI | Ensure training data quality and appropriateness | Data quality; lineage; bias detection; consent |
| Explainability | Make AI decisions understandable | Explainable AI (XAI) techniques; decision documentation |
| Human oversight | Ensure human review for high-stakes decisions | Human-in-the-loop processes; override mechanisms |
| Compliance | Comply with AI regulations (EU AI Act, etc.) | Regulatory mapping; compliance monitoring; reporting |
CDMP Exam Relevance
AI governance is an emerging topic in the CDMP exam, primarily tested in the Big Data & Data Science knowledge area (6%) and Data Ethics (2%). Key exam topics include: the definition and principles of AI governance, the relationship between AI governance and data governance, the concept of algorithmic bias and how to address it, and the role of data lineage in AI explainability. As AI governance regulations (EU AI Act, etc.) become more prominent, this topic is likely to increase in CDMP exam weight.
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