risk
Poor-quality, unrepresentative or mislabeled training data
Training data that is too small, unrepresentative, error-contaminated, or mislabeled produces systematic failure modes; data-lifecycle risks (unlawful collection, insecure storage, failure to purge) further corrupt model quality or violate privacy.
In catalog since 2026-09-17T22:28:00Z · Last changed 2026-09-17T22:28:00Z (f368a6cce277)
Record JSON · Open in map · Data retrieval guide
Catalog revision: 791ff2dd3a45707290badee660f185e514d15f1cf518908628f425c2f2c56ee4. A connection does not establish full coverage.
Attributes
- category
- ai_governance
- domain
- AI Governance
- Data Protection & Privacy
- taxonomy
- iso-23894-ai-risk
- eu-ai-act-risk
- inherent_rating
- high
Details
- risk_id
- ai-poor-training-data-quality
- category
- ai_governance
- likelihood
- high
- impact
- high
- inherent_rating
- high
- treatment
- mitigate
- taxonomies
- iso-23894-ai-risk
- eu-ai-act-risk
Source
No record-specific source URL is provided.
Connections
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Poor-quality, unrepresentative or mislabeled training data
- strength
- primary
- rationale
- Quality criteria (representativeness, completeness, error-freedom) plus cleaning/labelling governance directly prevents small, unrepresentative or mislabeled training data.
- UC-AI-14 — Manage responsible AI with suppliers and customers mitigates Poor-quality, unrepresentative or mislabeled training data
- strength
- related
- rationale
- Governing supplier-provided data verifies its quality before it is used in models.