EN ISO/IEC 23053:2023

SLOVENSKI STANDARD
01-november-2023
Okvir za sisteme umetne inteligence (UI), ki temeljijo na strojnem učenju (ISO/IEC
23053:2022)
Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) (ISO/IEC
23053:2022)
Framework für Systeme der Künstlichen Intelligenz (KI) basierend auf maschinellem
Lernen (ML) (ISO/IEC 23053:2022)
Cadre méthodologique pour les systèmes d'intelligence artificielle (IA) utilisant
l'apprentissage machine (ISO/IEC 23053:2022)
Ta slovenski standard je istoveten z: EN ISO/IEC 23053:2023
ICS:
35.020 Informacijska tehnika in Information technology (IT) in
tehnologija na splošno general
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.

EUROPEAN STANDARD EN ISO/IEC 23053

NORME EUROPÉENNE
EUROPÄISCHE NORM
June 2023
ICS 35.020
English version
Framework for Artificial Intelligence (AI) Systems Using
Machine Learning (ML) (ISO/IEC 23053:2022)
Cadre méthodologique pour les systèmes d'intelligence Framework für Systeme der Künstlichen Intelligenz
artificielle (IA) utilisant l'apprentissage machine (KI) basierend auf maschinellem Lernen (ML) (ISO/IEC
(ISO/IEC 23053:2022) 23053:2022)
This European Standard was approved by CEN on 26 June 2023.

CEN and CENELEC members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for
giving this European Standard the status of a national standard without any alteration. Up-to-date lists and bibliographical
references concerning such national standards may be obtained on application to the CEN-CENELEC Management Centre or to
any CEN and CENELEC member.
This European Standard exists in three official versions (English, French, German). A version in any other language made by
translation under the responsibility of a CEN and CENELEC member into its own language and notified to the CEN-CENELEC
Management Centre has the same status as the official versions.

CEN and CENELEC members are the national standards bodies and national electrotechnical committees of Austria, Belgium,
Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy,
Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Republic of North Macedonia, Romania, Serbia,
Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and United Kingdom.

CEN-CENELEC Management Centre:
Rue de la Science 23, B-1040 Brussels
© 2023 CEN/CENELEC All rights of exploitation in any form and by any means
Ref. No. EN ISO/IEC 23053:2023 E
reserved worldwide for CEN national Members and for
CENELEC Members.
Contents Page
European foreword . 3

European foreword
The text of ISO/IEC 23053:2022 has been prepared by Technical Committee ISO/IEC JTC 1 "Information
technology” of the International Organization for Standardization (ISO) and has been taken over as
secretariat of which is held by DS.
This European Standard shall be given the status of a national standard, either by publication of an
identical text or by endorsement, at the latest by December 2023, and conflicting national standards
shall be withdrawn at the latest by December 2023.
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. CEN-CENELEC shall not be held responsible for identifying any or all such patent rights.
Any feedback and questions on this document should be directed to the users’ national standards body.
A complete listing of these bodies can be found on the CEN and CENELEC websites.
According to the CEN-CENELEC Internal Regulations, the national standards organizations of the
following countries are bound to implement this European Standard: Austria, Belgium, Bulgaria,
Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland,
Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Republic of
North Macedonia, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the
United Kingdom.
Endorsement notice
The text of ISO/IEC 23053:2022 has been approved by CEN-CENELEC as EN ISO/IEC 23053:2023
without any modification.
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 23053:2022(E)
© ISO/IEC 2022
ISO/IEC 23053:2022(E)
© 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
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
ii
© ISO/IEC 2022 – All rights reserved

ISO/IEC 23053:2022(E)
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 .
...