ISO/IEC 23053:2022

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)

This document establishes an Artificial Intelligence (AI) and Machine Learning (ML) framework for describing a generic AI system using ML technology. The framework describes the system components and their functions in the AI ecosystem. This document is applicable to all types and sizes of organizations, including public and private companies, government entities, and not-for-profit organizations, that are implementing or using AI systems.

Cadre pour les systèmes d'intelligence artificielle (IA) qui utilisent l'apprentissage machine (ML)

Le présent document établit un cadre en matière d'intelligence artificielle (IA) et d'apprentissage machine (ML) pour la description d'un système d'IA générique utilisant la technologie du ML. Le cadre décrit les composants du système et leurs fonctions dans l'écosystème de l'IA. Le présent document s'applique aux organismes de tous types et de toutes tailles, y compris les entreprises publiques et privées, les entités gouvernementales et les organisations à but non lucratif, qui mettent en œuvre ou utilisent des systèmes d'IA.

General Information

• Status: Published
• Publication Date: 19-Jun-2022
• ICS: 35.020 - Information technology (IT) in general
• Technical Committee: ISO/IEC JTC 1/SC 42 - Artificial intelligence
• Drafting Committee: ISO/IEC JTC 1/SC 42/WG 1 - Foundational standards
• Current Stage: 6060 - International Standard published
• Start Date: 20-Jun-2022
• Due Date: 07-Mar-2022
• Completion Date: 20-Jun-2022

Standards Content (Sample)

INTERNATIONAL ISO/IEC
STANDARD 23053
First edition
2022-06
Framework for Artificial Intelligence
(AI) Systems Using Machine Learning
(ML)
Cadre méthodologique pour les systèmes d’intelligence artificielle (IA)
utilisant l’apprentissage machine
Reference number
© ISO/IEC 2022
© ISO/IEC 2022
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
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Published in Switzerland
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© ISO/IEC 2022 – All rights reserved

Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
3.1 Model development and use . 1
3.2 Tools . 2
3.3 Data . 2
4 Abbreviated terms . 3
5 Overview . 4
6 Machine learning system .4
6.1 Overview . 4
6.2 Task . 5
6.2.1 General . 5
6.2.2 Regression . 6
6.2.3 Classification . . 6
6.2.4 Clustering . . 6
6.2.5 Anomaly detection . . 6
6.2.6 Dimensionality reduction . 7
6.2.7 Other tasks . 7
6.3 Model . 7
6.4 Data . 8
6.5 Tools . 9
6.5.1 General . 9
6.5.2 Data preparation . 9
6.5.3 Categories of ML algorithms . 10
6.5.4 ML optimisation methods . 14
6.5.5 ML evaluation metrics . 16
7 Machine learning approaches .19
7.1 General . 19
7.2 Supervised machine learning . 20
7.3 Unsupervised machine learning . 22
7.4 Semi-supervised machine learning. 23
7.5 Self-supervised machine learning . 23
7.6 Reinforcement machine learning . 23
7.7 Transfer learning . 24
8 Machine learning pipeline .25
8.1 General . 25
8.2 Data acquisition .26
8.3 Data preparation . 27
8.4 Modelling . 28
8.5 Verification and validation .30
8.6 Model deployment .30
8.7 Operation . 30
8.8 Example machine learning process based on ML pipeline . 31
Annex A (informative) Example data flow and data use statements for supervised learning
process .34
Bibliography .36
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© ISO/IEC 2022 – All rights reserved

Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical
activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international
organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the
work.
The procedures used to develop this document and those intended for its further maintenance
are described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria
needed for the different types of document should be noted. This document was drafted in
accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives or
www.iec.ch/members_experts/refdocs).
Attention is drawn to the possibility that some of the elements of this document may be the subject
of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent
rights. Details of any patent rights identified during the development of the document will be in the
Introduction and/or on the ISO list of patent declarations received (see www.iso.org/patents) or the IEC
list of patent declarations received (see patents.iec.ch).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and
expressions related to conformity assessment, as well as information about ISO's adherence to
the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see
www.iso.org/iso/foreword.html. In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 42, Artificial Intelligence.
Any feedback or questions on this document should be directed to the user’s national standards
body. A complete listing of these bodies can be found at www.iso.org/members.html and
www.iec.ch/national-committees.
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© ISO/IEC 2022 – All rights reserved

Introduction
Artificial intelligence (AI) systems, in general, are engineered systems that generate outputs such as
content, forecasts, recommendations or decisions for a given set of human-defined objectives. AI covers
a wide range of technologies that reflect different approaches to dealing with these complex problems.
ML is a branch of AI that employs computational techniques to enable systems to learn from data or
experiences. In other words, ML systems are developed through the optimisation of algorithms to fit to
training data, or improve their performance based through maximizing a reward. ML methods include
deep learning, which is also addressed in this document.
Terms such as knowledge, learning and decisions are used throughout the document. However, it is not
the intent to anthropomorphize machine learning (ML).
This document aims to provide a framework for the description of AI systems that use ML. By
establishing a common terminology and a common set of concepts for such systems, this document
provides a basis for the clear explanation of the systems and various considerations that apply to their
engineering and to their use. This document is intended for a wide audience including experts and non-
practitioners. However, some of the clauses (identified in the overview in Clause 5), include more in-
depth technical descriptions.
This document also provides the basis for other standards directed at specific aspects of ML systems
and their components.
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INTERNATIONAL STANDARD ISO/IEC 23053:2022(E)
Framework for Artificial Intellig
...