Data governance
Veri yönetişimi
D5
Data governance is the organizational practice of managing data used in a stated AI context, including collection, origin, preparation, suitability, possible bias examination, and lifecycle traceability.
Review status: 2027-02-24
Technical explanation
It makes data collection, origin, preparation, assumptions, suitability, possible bias examination, and traceability inspectable for the stated context.
Conceptual boundaries
Data governance is not dataset documentation alone, a database feature alone, or a guarantee that data are accurate, fair, lawful, or private.
Provider-neutral example
Before using training data in a model workflow, a team can record its origin and preparation, state what it is intended to represent, assess its suitability, and examine relevant possible bias.
Limitations
Traceability supports analysis and accountability but does not prove that particular data are accurate, fair, lawful, or private.
Related concepts
Atomic claims and evidence
1.1Regulation (EU) 2024/1689 (Artificial Intelligence Act), consolidated text of 27 July 2026 — D5 instrument slice
- Source
- Regulation (EU) 2024/1689 (Artificial Intelligence Act), consolidated text of 27 July 2026 — D5 instrument slice
- Source role
- Authoritative source
- Exact locator
- Article 10(1)-(2), consolidated text of 27 July 2026
- Supported claim
- For high-risk AI systems using data to train models, the EU AI Act requires data governance and management practices appropriate to intended purpose, including collection, origin, preparation, assumptions, suitability, and possible bias examination.
- Last verification
- Review due
- Scope limitation
- This is an EU instrument-specific requirement for the stated high-risk systems, not a complete general definition of data governance.
1.2OECD AI Principles
- Source
- OECD AI Principles
- Source role
- Authoritative source
- Exact locator
- Accountability
- Supported claim
- For high-risk AI systems using data to train models, the EU AI Act requires data governance and management practices appropriate to intended purpose, including collection, origin, preparation, assumptions, suitability, and possible bias examination.
- Last verification
- Review due
- Scope limitation
- This is an EU instrument-specific requirement for the stated high-risk systems, not a complete general definition of data governance.
1.3European Commission, AI Act regulatory framework
- Source
- European Commission, AI Act regulatory framework
- Source role
- Supplementary source
- Exact locator
- High risk, supplementary summary
- Supported claim
- For high-risk AI systems using data to train models, the EU AI Act requires data governance and management practices appropriate to intended purpose, including collection, origin, preparation, assumptions, suitability, and possible bias examination.
- Last verification
- Review due
- Scope limitation
- This is an EU instrument-specific requirement for the stated high-risk systems, not a complete general definition of data governance.
2.1OECD AI Principles
- Source
- OECD AI Principles
- Source role
- Authoritative source
- Exact locator
- Accountability
- Supported claim
- The OECD AI Principles call for traceability in relation to datasets, processes, and decisions during the AI system lifecycle as appropriate to context.
- Last verification
- Review due
- Scope limitation
- Traceability supports analysis and accountability; it does not prove that a particular dataset is accurate, lawful, fair, or private.
2.2Regulation (EU) 2024/1689 (Artificial Intelligence Act), consolidated text of 27 July 2026 — D5 instrument slice
- Source
- Regulation (EU) 2024/1689 (Artificial Intelligence Act), consolidated text of 27 July 2026 — D5 instrument slice
- Source role
- Authoritative source
- Exact locator
- Article 10(2)(b)-(e), consolidated text of 27 July 2026
- Supported claim
- The OECD AI Principles call for traceability in relation to datasets, processes, and decisions during the AI system lifecycle as appropriate to context.
- Last verification
- Review due
- Scope limitation
- Traceability supports analysis and accountability; it does not prove that a particular dataset is accurate, lawful, fair, or private.