DG-008MedioFornitorePreventivo
Versionamento e controllo delle modifiche dei dati
I fornitori devono mantenere registrazioni versionate dei set di dati di addestramento, validazione e collaudo utilizzati in ciascuna versione del sistema di IA, consentendo riproducibilità dei risultati e tracciabilità delle modifiche dei dati che possono incidere sul comportamento del sistema.
Articoli:Article 10(2)Article 12(1)
Esempi di prove
- Dataset version registry
- Data change log
- Dataset hash/fingerprint records
Norme
ISO 42001:2023 §8.2ISO/IEC 5259-3
Controlli correlati
- 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…
Termini correlati del Regolamento IA
- 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.
Passa alla versione completa quando la conformità deve essere
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