DG-003HochAnbieterPräventiv
Dokumentation und Herkunft der Daten
Anbieter müssen Ursprung, Erhebungsmethodik, Kennzeichnungsprozess und relevante Merkmale aller für Training, Validierung und Tests verwendeten Datensätze dokumentieren, um Nachvollziehbarkeit und Reproduzierbarkeit zu ermöglichen.
Artikel:Article 10(2)Article 11(1)
Nachweisbeispiele
- Data provenance record
- Dataset data sheet
- Collection methodology description
Normen
ISO 42001:2023 §8.2ISO/IEC 5259-3
Verwandte Kontrollen
- 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,…
Verwandte KI-VO-Begriffe
- 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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