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)

ISO/IEC 12792:2025 – Information technology – Artificial intelligence (AI) – Transparency taxonomy of AI systems

Provides definitions of taxonomy of information elements to assist AI stakeholders with identifying and addressing the needs for transparency of AI systems.

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.

ISO/IEC DIS 42007– Artificial intelligence – High-level framework and guidance for the development of conformity assessment schemes for AI systems

Status: Draft International Standard, developed by the joint working group ISO/IEC JTC 1/SC 42/JWG 6 together with ISO/CASCO. Comments can be submitted via the national standards bodies until 3 November 2026.

This project is of particular interest for legal metrology, since it addresses how conformity assessment schemes for AI systems are to be designed. The DTG Subgroup encourages OIML Member States to contribute through their national standards bodies.

CEN/CLC/TR 17894:2024 – Artificial Intelligence – Artificial Intelligence Conformity Assessment

Technical report prepared by CEN/CLC/JTC 21 on conformity assessment of AI systems in the European context.

Further relevant standards and guidelines

NIST AI Risk Management Framework v1.10

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. NIST has since complemented the framework with the Generative AI Profile (NIST AI 600-1, 2024) and, in April 2026, published a concept note for an AI RMF profile on trustworthy AI in critical infrastructure, which is relevant for utility metering.

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.

WELMEC Guide 7.2:2026 – Software Guide

Current version of the European guide for software examination of measuring instruments under the MID and NAWID. It follows a risk-based approach and is a natural reference point for assessing measuring instrument software that incorporates AI components.

DTG SG-AI, October 2026