Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

ISO/IEC 25023 - Application of ISO 14971 to machine learning in artificial intelligence. Guide



ISO/IEC 25023:2016 defines quality measures for quantitatively evaluating system and software product quality in terms of characteristics and subcharacteristics defined in ISO/IEC 25010 and is intended to be used together with ISO/IEC 25010. It can be used in conjunction with the ISO/IEC 2503n and the ISO/IEC 2504n standards or to more generally meet user needs with regard to software product or system quality. 
ISO/IEC 25023:2016 contains the following:

  • a basic set of quality measures for each characteristic and subcharacteristics;
  • an explanation of how to apply software product and system quality measures. 

It includes, as informative annexes, considerations for the use of quality measures (Annex A), QMEs used to define product or system quality measures (Annex B), and detailed explanation of measurement types (Annex C). 
ISO/IEC 25023:2016 does not assign ranges of values of the measures to rated levels or to grades of compliance because these values are defined based on the nature of the system, product or a part of the product, and depending on factors such as category of the software, integrity level, and users' needs. Some attributes could have a desirable range of values, which does not depend on specific user needs but depends on generic factors; for example, human cognitive factors. The proposed quality measures are primarily intended to be used for quality assurance and improvement of system and software products during or post the development life cycle process. 
The main users of ISO/IEC 25023:2016 are people carrying out quality requirement specification and evaluation activities as part of the following:

  • development: including requirements analysis, design specification, coding and testing through acceptance during the life cycle process;
  • quality management: systematic examination of the software product or computer system, for example, when evaluating system or software product quality as part of quality assurance, quality control and quality certification;
  • supply: a contract with the acquirer for the supply of a system, software product or software service under the terms of a contract, for example, when validating quality at qualification test;
  • acquisition: including product selection and acceptance testing, when acquiring or procuring a system, software product or software service from a supplier;
  • maintenance: improvement of the software product or system based on quality measurement. 

© ISO/IEC 2022 All rights reserved

The information about this standard has been compiled by the AI Standards Hub, an initiative dedicated to knowledge sharing, capacity building, research, and international collaboration in the field of AI standards. You can find more information and interactive community features related to this standard by visiting the Hub’s AI standards database here. To access the standard directly, please visit the developing organisation’s website.

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.