Data Governance for Training and Testing
This topic covers lawful data rights, data quality, data quantity, integrity, fit-for-purpose, lineage, provenance, training and testing plans, validation, performance, security, bias, interpretability, risk handling, and documentation.
How to study for IAPP AIGP
Treat each question as a governance decision: identify the AI role and life-cycle stage, classify the legal or risk issue, then choose the control, evidence, and accountability path.
Core concepts
Concept 1
AI training and testing require evidence that data can be used lawfully and is suitable in quality, quantity, integrity, and fit for purpose.
Exam cue: Use data provenance when the question asks where data came from or whether it may be used.
Concept 2
Data lineage and provenance support explainability, accountability, issue investigation, and rights management.
Exam cue: Use fit-for-purpose when data quality must match the intended use case.
Concept 3
Testing should include validation, performance, security, bias, interpretability, and documented risk handling.
Exam cue: Use bias and interpretability testing when a system may create uneven or opaque outcomes.
Risk pitfalls and guardrails
Training on data without documenting rights and provenance.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Using a large dataset that is not representative of the target context.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Testing only average model performance while ignoring security and bias.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Memory anchors
Data Rights
Data rights determine whether data may be collected, used, shared, or retained for AI purposes.
Data Quality
Data quality covers accuracy, completeness, consistency, timeliness, and reliability.
Fit-for-Purpose
Fit-for-purpose data is suitable for the intended AI use and operating context.
Data Lineage
Data lineage tracks data origin, movement, transformations, and use.
Provenance
Provenance documents the source and history of data or model artifacts.
Validation Testing
Validation testing checks whether the model or system performs as intended.
Bias Testing
Bias testing looks for systematic unfairness or uneven performance across groups.
Testing Documentation
Testing documentation records methods, results, issues, decisions, and approvals.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
Why is the quality of data labels important for supervised learning?
Why is inter-annotator agreement useful when creating labeled data?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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