AI Governance
ISO/IEC 5259
Data Quality for Analytics and Machine Learning
A series covering data quality for analytics and machine learning: vocabulary, quality measures, management requirements, a process framework, and governance of the data used to train and evaluate models.
It is the standards work that most directly addresses what "relevant, representative, free of errors and complete" is supposed to mean when a regulator says it about training data.
At a glance
- Category
- AI Governance
- Jurisdiction
- Global
- Governance
- ISO/IEC JTC 1/SC 42
- Status
- Multi-part series; core parts published from 2024
- First released
- 2024
Links
Related frameworks
Other entries under AI Governance.
- ISO/IEC 42001: Artificial Intelligence Management System
- ISO/IEC 23894: AI Risk Management Guidance
- NIST AI RMF: AI Risk Management Framework
- JTC 21: CEN-CENELEC JTC 21: Harmonised European Standards for AI
See ISO/IEC 5259 in context
Open the interactive Data Landscape for Regulation to compare ISO/IEC 5259 against every other framework, or grab the raw JSON. Certification schemes and editions move — follow the source links before relying on this page.