RequirementsArticle 10
Data Governance
Practices 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.
Related terms
- Risk Management SystemA continuous iterative process that must be established, implemented, documented, and maintained by providers of high-risk AI systems throughout the…
- Human OversightMeasures built into high-risk AI systems enabling natural persons to understand, monitor, and — where necessary — override or shut down the system. Must…
- AccuracyThe requirement that high-risk AI systems achieve an appropriate level of accuracy in relation to their intended purpose, as specified in the technical…
- RobustnessThe ability of a high-risk AI system to maintain its level of performance under adverse conditions — including technical limitations, adversarial inputs,…
- CybersecurityThe requirement that high-risk AI systems are resilient against attempts by third parties to alter their use, behaviour, or performance in ways that could…
- Transparency RequirementsObligations under Article 13 requiring that high-risk AI systems are designed to ensure that their operation is sufficiently transparent to enable…
Related compliance controls
- RM-012Training Data Risk AssessmentProviders must assess risks arising from the training data used to develop the AI system, including risks of bias, data quality defects, and gaps in representativeness that could affect the system's performance on affected persons.
- DG-001Training Data Quality RequirementsArticle 10(3) requires that training, validation, and testing data sets are subject to data governance practices that ensure relevance, representativeness, and freedom from errors to the extent possible given the intended purpose.
- DG-002Bias Detection and CheckingProviders must examine training, validation, and testing datasets for possible biases that could affect health, safety, or fundamental rights, and implement appropriate bias mitigation measures before and during model training.
- DG-003Data Documentation and ProvenanceProviders 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.
TMP-AI-DATAFree Download · Article 10
Data Governance & Quality Controls
Control training and test data quality, relevance, representativeness, bias checks, annotation quality, privacy safeguards and versioning.
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