National foreword

This British Standard is the UK implementation of EN ISO/IEC 5259‑4:2025. It is identical to ISO/IEC 5259‑4:2024. It supersedes BS ISO/IEC 5259‑4:2024, which is withdrawn.

The UK participation in its preparation was entrusted to Technical Committee ART/1, Artificial Intelligence.

A list of organizations represented on this committee can be obtained on request to its committee manager.

Contractual and legal considerations

This publication has been prepared in good faith, however no representation, warranty, assurance or undertaking (express or implied) is or will be made, and no responsibility or liability is or will be accepted by BSI in relation to the adequacy, accuracy, completeness or reasonableness of this publication. All and any such responsibility and liability is expressly disclaimed to the full extent permitted by the law.

This publication is provided as is, and is to be used at the recipient’s own risk.

The recipient is advised to consider seeking professional guidance with respect to its use of this publication.

This publication is not intended to constitute a contract. Users are responsible for its correct application.

Compliance with a British Standard cannot confer immunity from legal obligations.

This British Standard was published under the authority of the Standards Policy and Strategy Committee on 31 July 2024.

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).

ISO and IEC draw attention to the possibility that the implementation of this document may involve the use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not received notice of (a) patent(s) which may be required to implement this document. However, implementers are cautioned that this may not represent the latest information, which may be obtained from the patent database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held responsible for identifying any or all such patent rights.

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.

A list of all parts in the ISO/IEC 5259 series can be found on the ISO and IEC websites.

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.

Introduction

Artificial intelligence (AI)-related products, systems or solutions have developed quickly in recent years. One of the common characteristics of an AI system, especially for systems using supervised machine learning (ML), is whether the AI system can be trained on a dataset before deployment or trained dynamically as the system is used.

Data have been recognized as one of the most important aspects of ML-based AI systems. For all supervised, semi-supervised, unsupervised and reinforcement learning approaches, data quality can be a primary concern in creating and using data for training and evaluating ML systems. It has been shown that with more accurate and richer data, the results of analytics and ML can be more useful and reliable. In addition, for the development of supervised learning-based AI systems, a large number of task-specific labelled training data is needed. This makes accurately labelled data one of the most important resources in the AI industry. Nowadays, there is a verified market of industrial services and tools for training data labelling. This market is now reaching a level of maturity that justifies the development of International Standards for the benefit of providers and users of these services and tools to ensure high-quality labelled data.

This document describes the implementation of a standardized common procedure of data processing with regard to data quality for analytics and ML. Clause 5 describes principles about data quality process and Clause 6 describes a data quality process framework. Clause 7 describes the data quality process for ML approaches, Clause 8 describes data labelling methods and process, Clause 9 provides roles of participants in data quality processes, Clauses 10 and 11 then describe the additional considerations that apply to semi-supervised learning and reinforcement learning. Clause 12 describes how the data quality process framework applies to analytics.

This document provides the process framework on a detailed level which can be used to fulfil the requirements specified in ISO/IEC 5259‑3. It also links processes that are mapped on the data life cycle model in ISO/IEC 5259‑1.

This European Standard was approved by CEN on 18 May 2025.

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, Czechia, 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.

European foreword

The text of ISO/IEC 5259‑4:2024 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 EN ISO/IEC 5259‑4:2025 by Technical Committee CEN-CENELEC/ JTC 21 “Artificial Intelligence” the 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 November 2025, and conflicting national standards shall be withdrawn at the latest by November 2025.

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 5259‑4:2024 has been approved by CEN-CENELEC as EN ISO/IEC 5259‑4:2025 without any modification.

1 Scope

This document establishes general common organizational approaches, regardless of the type, size or nature of the applying organization, to ensure data quality for training and evaluation in analytics and machine learning (ML). It includes guidance on the data quality process for:

supervised ML with regard to the labelling of data used for training ML systems, including common organizational approaches for training data labelling;
unsupervised ML;
semi-supervised ML;
reinforcement learning;
analytics.

This document is applicable to training and evaluation data that come from different sources, including data acquisition and data composition, data preparation, data labelling, evaluation and data use. This document does not define specific services, platforms or tools.

2 Normative references

3 Terms and definitions

4 Symbols and abbreviated terms

5 Data quality process principles

6 Data quality process framework

6.1 General

6.2 Data quality planning

6.3 Data quality evaluation

6.4 Data quality improvement

6.5 Data quality process validation

6.6 Using the DQPF

7 Data quality process for ML

7.1 General

7.2 Data requirements

7.3 Data planning

7.4 Data acquisition

7.5 Data preparation

7.6 Data provisioning

7.7 Data decommissioning

8 Data labelling methods and process

8.1 General

8.2 Data labelling principles

8.3 Data labelling methods

8.4 Data labelling process

9 Roles of participants

9.1 General

9.2 Data planner

9.3 Data originator

9.4 Data collector

9.5 Data engineer

9.6 Data holder

9.7 Data user

10 Data quality process for semi-supervised ML

10.1 General

10.2 Data requirements

10.3 Data planning

10.4 Data acquisition

10.5 Data preparation

10.6 Data provisioning

10.7 Data decommissioning

11 Data quality process for reinforcement learning

11.1 General

11.2 Data requirements

11.3 Data planning

11.4 Data acquisition

11.5 Data preparation

11.6 Data provisioning

11.7 Data decommissioning

12 Data quality process for analytics

12.1 General

12.2 Data requirements

12.3 Data planning

12.4 Data acquisition

12.5 Data preparation

12.6 Data provisioning

12.7 Data decommissioning

Bibliography

BS EN ISO/IEC 5259-4:2025

Artificial intelligence. Data quality for analytics and machine learning (ML) - Data quality process framework

Current

Published:

30 Jun 2025