DG-006MediumProviderDetective
Data Annotation Quality Assurance
Where data labelling or annotation is performed, providers must implement quality assurance procedures to ensure consistency, accuracy, and reproducibility of annotations, including inter-annotator agreement measurement.
Articles:Article 10(2)(b)Article 10(3)
Evidence Examples
- Annotation guidelines document
- Inter-annotator agreement report
- Annotation QA audit records
Standards
ISO 42001:2023 §8.2ISO/IEC 5259-1
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-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-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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