DG-008GemiddeldAanbiederPreventief
Dataversionering en wijzigingsbeheer
Aanbieders moeten geversioneerde registraties bijhouden van trainings-, validatie- en testgegevenssets die in elke versie van het AI-systeem zijn gebruikt, zodat resultaten reproduceerbaar zijn en gegevenswijzigingen die systeemgedrag kunnen beïnvloeden traceerbaar zijn.
Artikelen:Article 10(2)Article 12(1)
Bewijs voorbeelden
- Dataset version registry
- Data change log
- Dataset hash/fingerprint records
Normen
ISO 42001:2023 §8.2ISO/IEC 5259-3
Gerelateerde controles
- 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-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…
Gerelateerde AI-verordening-begrippen
- Logging CapabilitiesThe automatic recording of events by a high-risk AI system during its operation — required under Article 12 to enable monitoring of its operation, post-hoc investigation of incidents, and to support the post-market monitoring obligations of providers and deployers.
- Data GovernancePractices and policies applicable to training, validation, and testing data sets used for high-risk AI systems, covering the design choices, data collection, and preparation processes, and ensuring datasets are relevant, sufficiently representative, and free of errors and complete.
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