DG-001CriticalProviderPreventive
Training Data Quality Requirements
Article 10(3) requires that training, validation, and testing data sets are subject to data governance practices that ensure relevance, representativeness, and freedom from errors to the extent possible given the intended purpose.
Articles:Article 10(3)Article 10(2)(a)
Evidence Examples
- Training data quality policy
- Data quality inspection report
- Acceptance criteria documentation
Standards
ISO 42001:2023 §8.2ISO/IEC 5259
Related controls
- DG-002Bias Detection and CheckingProviders must examine training, validation, and testing datasets for possible biases that could affect health, safety, or fundamental rights, and…
- DG-003Data Documentation and ProvenanceProviders must document the origin, collection methodology, labelling process, and relevant characteristics of all data sets used in training, validation,…
- DG-004Data Representativeness AssessmentArticle 10(3) requires that training data sets are sufficiently representative of the intended population and use-case context, and providers must…
- DG-005Data Relevance and Completeness CheckProviders must verify that data sets used for high-risk AI systems are relevant and complete for the system's intended purpose, documenting any known gaps…
- DG-006Data Annotation Quality AssuranceWhere data labelling or annotation is performed, providers must implement quality assurance procedures to ensure consistency, accuracy, and…
- DG-007Privacy-Preserving Data TechniquesWhen processing personal data for AI training, providers must implement appropriate privacy-preserving techniques such as pseudonymisation, anonymisation,…
Related EU AI Act terms
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