Apply data governance and quality criteria to training, validation and testing data
Under EU AI Act, Article 10
What does it require?
High-risk AI systems that use data-driven techniques must be developed on training, validation and testing data sets meeting quality criteria: appropriate governance practices covering design choices, data collection and origin, preparation, assumptions, availability and suitability, examination for possible biases, and measures to detect, prevent and mitigate bias. Data must be relevant, sufficiently representative and, to the best extent possible, free of errors and complete for the intended purpose.
Practical action
Produce a data-provenance and bias-assessment record for each data set used to build the system.
Who does it apply to?
Providers of high-risk AI systems that involve model training.
- Actors
- Provider / developer
- Sectors
- Cross-sector / all sectors
Applies from:
Evidence examples
- Dataset documentation (datasheet) (document)
- Bias examination report (report)
Framework mappings
Original editorial crosswalks. They cite clause numbers only and reproduce no standard text; confidence reflects how direct the mapping is.
| Framework | Reference | Note | Confidence |
|---|---|---|---|
| ISO/IEC 42001:2023 | Annex A controls on data for AI systems | Original editorial mapping. | medium |
| NIST AI RMF 1.0 | MAP 2.3, MEASURE 2.1, MEASURE 2.11 | Data quality and bias measurement. | medium |
Similar obligations in other instruments
- Manage data quality, model development and monitoring across the lifecycle — Singapore Model AI Governance Framework, Singapore (voluntary)
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.