International standards and frameworks that guide trustworthy AI and data governance.
The international dictionary of AI — defining what words like 'machine learning', 'neural network', and 'explainability' mean in a technically precise and internationally agreed way.
An international standard for describing how machine learning systems are built and work — a common blueprint language for explaining ML architecture to diverse stakeholders.
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.
An international standard for boards and senior executives on how to govern an organisation's use of AI — covering oversight, accountability, and strategic direction.
The internationally recognised standard for managing AI responsibly — a certifiable management system that tells organisations how to govern AI across its lifecycle.
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.
The definitions part of the ISO/IEC 5259 standard — establishing the shared vocabulary for talking about data quality in AI and analytics contexts.
The measurement part of the ISO/IEC 5259 standard — specifying how to quantitatively assess different aspects of data quality for AI.
The management requirements part of ISO/IEC 5259 — specifying what an organisation needs to put in place to systematically manage data quality for AI.
The process blueprint part of ISO/IEC 5259 — describing the specific activities organisations should follow to build and maintain high-quality data for AI.
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.
A widely used US government framework that helps organisations manage AI risks in a structured way — covering governance, risk identification, measurement, and ongoing management.
Internationally agreed principles from the OECD for how AI should be developed and used responsibly — covering fairness, transparency, accountability, and safety.
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.
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.