DG-003HighProviderPreventive
Data Documentation and Provenance
Providers must document the origin, collection methodology, labelling process, and relevant characteristics of all data sets used in training, validation, and testing to enable traceability and reproducibility.
Articles:Article 10(2)Article 11(1)
Evidence Examples
- Data provenance record
- Dataset data sheet
- Collection methodology description
Standards
ISO 42001:2023 §8.2ISO/IEC 5259-3
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-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
- Technical DocumentationThe documentation that providers of high-risk AI systems must draw up before placing the system on the market, containing all necessary information to assess compliance with the Regulation, including a general description, design specifications, training data information, risk management records, and performance metrics. Content requirements are set out in Annex IV.
- 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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