DG-005HighProviderDetective
Data Relevance and Completeness Check
Providers 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 and their potential impact on system performance.
Articles:Article 10(3)Article 10(2)(d)
Evidence Examples
- Data completeness checklist
- Relevance assessment report
- Coverage gap register
Standards
ISO 42001:2023 §8.2ISO/IEC 5259
Related controls
- DG-001Training Data Quality RequirementsArticle 10(3) requires that training, validation, and testing data sets are subject to data governance practices that ensure relevance,…
- 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-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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