Standards & Governance Frameworks Glossary

International standards and frameworks that guide trustworthy AI and data governance.

ISO/IEC 22989

The international dictionary of AI, defining what words like 'machine learning', 'neural network', and 'explainability' mean in a technically precise and internationally agreed way.

ISO/IEC 23053

An international standard for describing how machine learning systems are built and work, a common blueprint language for explaining ML architecture to diverse stakeholders.

ISO/IEC 23894

An international guidance standard that helps organisations apply risk management principles to AI, addressing the unique risks that come from AI's non-determinism, opacity, and emergent behaviour.

ISO/IEC 38507

An international standard for boards and senior executives on how to govern an organisation's use of AI, covering oversight, accountability, and strategic direction.

ISO/IEC 42001

The internationally recognised standard for managing AI responsibly, a certifiable management system that tells organisations how to govern AI across its lifecycle.

ISO/IEC 5259

A family of international standards that sets out how to define, measure, manage, and govern data quality for AI systems, from shared vocabulary to process and governance models.

ISO/IEC 5259 Part 1: Terminology and Overview

The definitions part of the ISO/IEC 5259 standard, establishing the shared vocabulary for talking about data quality in AI and analytics contexts.

ISO/IEC 5259 Part 2: Data Quality Measures

The measurement part of the ISO/IEC 5259 standard, specifying how to quantitatively assess different aspects of data quality for AI.

ISO/IEC 5259 Part 3: Data Quality Management Requirements

The management requirements part of ISO/IEC 5259, specifying what an organisation needs to put in place to systematically manage data quality for AI.

ISO/IEC 5259 Part 4: Data Quality Process Framework

The process blueprint part of ISO/IEC 5259, describing the specific activities organisations should follow to build and maintain high-quality data for AI.

ISO/IEC 5259 Part 5: Data Quality Governance Framework

The governance part of ISO/IEC 5259, specifying how an organisation should structure its leadership, roles, and oversight to ensure data quality is treated as an enterprise-level responsibility.

NIST AI Risk Management Framework

A widely used US government framework that helps organisations manage AI risks in a structured way, covering governance, risk identification, measurement, and ongoing management.

OECD AI Principles

Internationally agreed principles from the OECD for how AI should be developed and used responsibly, covering fairness, transparency, accountability, and safety.

W3C PROV-O

A W3C web standard for recording where data and content came from, what processes created or modified it, and who was responsible, in a format machines can read and verify.

W3C SKOS

A W3C web standard for publishing structured vocabularies, glossaries, and taxonomies in a format that machines can read and link to each other across the web.