International Standards on AI
Standards on artificial intelligence are an important requirement for regulators, conformity assessment bodies and companies for a reliable and trustworthy AI future. In many regions, countries and economies, national standardization bodies are actively working on standards also related to AI and measurements.
Here we focus on international Standards The following overview is partially based on work carried out by the AIQI Consortium.
Fundamental aspects and terminology
-
ISO/IEC 22989:2022 – Artificial intelligence concepts and terminology
Core international standard defining AI, AI systems, bias, robustness, transparency, and other foundational AI terms
-
ISO/IEC TR 24372 – Overview of computational approaches for AI systems
Provides an overview of state-of-the-art computational approaches and algorithms used in AI systems.
-
ISO/IEC TS 5723:2022 – Trustworthiness: vocabulary
Provides basic terminologies and definitions in the field of trustworthiness of systems (not only AI)
Assessment of AI
-
ISO/IEC 42006:2025 – Information technology – Artificial intelligence – Requirements for bodies providing audit and certification of artificial intelligence management systems
This standard builds on ISO/IEC 17021. It addresses certification bodies that assess organizations developing, deploying or offering AI systems
-
ISO/IEC 29119-11 – Software testing – Part 11: Testing of AI systems
This document is part of a larger series on software testing. Part 11 provides technical specifications describing AI/ML assessment metrics applicable to AI system life cycle stages.
-
ISO/IEC 23053 – Framework for AI Systems Using Machine Learning
This document seeks to provide a unified vocabulary and model for AI systems that are based on machine learning methods.
-
ISO/IEC 42001:2023 – Artificial intelligence – Management system
This can be considered one of the primary AI governance standards. It specifies requirements for establishing and improving an AI management system within an organization, with a risk management framework and a policy for responsible use of AI.
Further relevant standards and guidelines
Defines characteristics of trustworthy AI (valid, reliable, safe, secure, accountable, explainable, privacy-enhanced, fair). This document also contains several definitions for terms relevant in AI and machine learning.
-
ISO/IEC TR 24030 – AI Use cases
This document provides a collection of artificial intelligence (AI) use cases across various domains.
-
ISO/IEC 25012:2008 – Data quality model
This document provides a general data quality model for data retained in a structured format within a computer system
-
ISO/IEC 5259-1 through 5259-6 – Data quality for analytics and machine learning
Part 1 of this series of standards on data quality focuses on AI and machine learning contexts.
-
ISO/IEC 23894 – Artificial intelligence – Guidance on risk management
Provides guidance for organisations developing or using AI products on managing AI-specific risk throughout the AI lifecycle.
-
ISO/IEC 6254 – Objectives and approaches for explainability of ML models and AI systems
Document about approaches and methods for achieving explainability of AI and machine learning.
DTG SG-AI, April 2026
