oSIST prEN ISO/IEC 25059:2026

SLOVENSKI STANDARD
01-marec-2026
Programsko inženirstvo - Zahteve za kakovost in vrednotenje sistemov in
programske opreme (SQuaRE) - Modeli kakovosti za sisteme UI (ISO/IEC DIS
25059:2025)
Software engineering - Systems and software Quality Requirements and Evaluation
(SQuaRE) - Quality models for AI systems (ISO/IEC DIS 25059:2025)
Software-Engineering - Systeme und Software Qualitätsanforderungen und Bewertung
(SQuaRE) - Qualitätsmodell für KI-Systeme (ISO/IEC DIS 25059:2025)
Ingénierie du logiciel - Exigences de qualité et évaluation des systèmes et du logiciel
(SQuaRE) - Modèles de qualité pour les systèmes d'IA (ISO/IEC DIS 25059:2025)
Ta slovenski standard je istoveten z: prEN ISO/IEC 25059
ICS:
35.080 Programska oprema Software
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.

DRAFT
International
Standard
ISO/IEC DIS 25059
ISO/IEC JTC 1/SC 42
Software engineering — Systems
Secretariat: ANSI
and software Quality Requirements
Voting begins on:
and Evaluation (SQuaRE) — Quality
2025-12-18
models for AI systems
Voting terminates on:
ICS: 35.080
2026-03-12
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Reference number
© ISO/IEC 2025
ISO/IEC DIS 25059:2025(en)
DRAFT
ISO/IEC DIS 25059:2025(en)
International
Standard
ISO/IEC DIS 25059
ISO/IEC JTC 1/SC 42
Software engineering — Systems
Secretariat: ANSI
and software Quality Requirements
Voting begins on:
and Evaluation (SQuaRE) — Quality
models for AI systems
Voting terminates on:
ICS: 35.080
THIS DOCUMENT IS A DRAFT CIRCULATED
FOR COMMENTS AND APPROVAL. IT
IS THEREFORE SUBJECT TO CHANGE
AND MAY NOT BE REFERRED TO AS AN
INTERNATIONAL STANDARD UNTIL
PUBLISHED AS SUCH.
This document is circulated as received from the committee secretariat.
IN ADDITION TO THEIR EVALUATION AS
BEING ACCEPTABLE FOR INDUSTRIAL,
© ISO/IEC 2025
TECHNOLOGICAL, COMMERCIAL AND
USER PURPOSES, DRAFT INTERNATIONAL
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
STANDARDS MAY ON OCCASION HAVE TO
ISO/CEN PARALLEL PROCESSING
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Published in Switzerland Reference number
© ISO/IEC 2025
ISO/IEC DIS 25059:2025(en)
© ISO/IEC 2025 – All rights reserved
ii
ISO/IEC DIS 25059:2025(en)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
3.1 General .1
3.2 Product quality .3
3.3 Quality-in-use .4
3.4 AI service quality .4
4 Abbreviated terms . 5
5 Product quality model . 5
5.1 General .5
5.2 User controllability .6
5.3 Functional adaptability.6
5.4 Functional correctness .6
5.5 Robustness .7
5.6 Transparency .7
5.7 Intervenability .7
5.8 Environmental sustainability .8
6 Quality-in-use model . 8
6.1 General .8
6.2 Transparency .9
6.3 Societal and ethical risk mitigation .9
7 AI service quality model .10
7.1 General .10
7.2 Traceability .10
7.3 Service adaptability .10
7.4 Customizability .11
Annex A (informative) SQuaRE.12
Annex B (informative) How a risk-based approach relates to a quality-based approach and
quality models . 14
Annex C (informative) Performance . 17
Bibliography .18

© ISO/IEC 2025 – All rights reserved
iii
ISO/IEC DIS 25059:2025(en)
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.
This second edition cancels and replaces the first edition (ISO/IEC 25059:2023), which has been technically
revised.
The main changes are as follows:
— alignment with the revised version of ISO/IEC 25010:2023;
— alignment with the revised version of ISO/IEC 25019:2023;
— addition of an AI service quality model based on ISO/IEC TS 25011:2017;
— addition of “Environmental sustainability” as a sub-characteristic of “Performance efficiency” in the
product quality model.
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.

© ISO/IEC 2025 – All rights reserved
iv
ISO/IEC DIS 25059:2025(en)
Introduction
High-quality software products and computer systems are crucial to stakeholders. Quality models, quality
requirements, quality measurement, and quality evaluation are standardized within the International
Standards on SQuaRE, see Annex A for further information.
AI systems require additional properties and characteristics of systems to be considered, and stakeholders
have varied needs. AI systems have different properties and characteristics. For example, AI systems can:
— replace human decision-making;
— be based on noisy, or incomplete data;
— be probabilistic;
— adapt during operation.
The concept of trustworthiness is addressed through a holistic ecosystem of standards. It is understood
both as an ongoing organizational process, guided by standards like ISO/IEC 42001 and as a set of non-
functional requirements specifying emergent properties of a system as described in ISO/IEC TR 24028.
[2]
These requirements — which are a set of inherent characteristics with their attributes — within the
context of quality of use as indicated in ISO/IEC 25010 (product quality model), ISO/IEC 25019 (quality-in-
use model) and ISO/IEC TS 25011 (service quality models).
A key consideration in the field is the applicability of standards developed for conventional software to AI
systems. It is widely recognized that existing frameworks do not sufficiently address the data-driven and
unpredictable nature of AI systems. While considering the existing body of work, experts have identified
the need to develop new international standards for AI systems that can go beyond the characteristics and
requirements of conventional software development.
A related discussion concerns different approaches to the testing and evaluation of AI systems. The prevailing
view is that for the testing of an AI system, modified versions of existing software and hardware verification
and validation techniques are needed. Due to several conceptual differences between many AI systems and
conventional systems, it has been concluded that “the ability of the [AI] system to achieve the planned and
desired result … [cannot] always be measurable by conventional approaches to software testing”. Testing of
AI systems is addressed in ISO/IEC 42119 series (42119-2 and DTS 42119-3).
This document outlines an application-specific AI system extension to the SQuaRE quality models specified
in ISO/IEC 25010, ISO/IEC 25019 and ISO/IEC TS 25011.
The AI system quality is considered from two perspectives (quality models), product quality as described
in Clause 5 and quality-in-use in Clause 6. The relevance of these terms is explained, and links to other
standardization deliverables (e.g. the ISO/IEC 24029 series) are highlighted.
AI service quality is also part of this document as users and organizations need high-quality support for
their AI systems. AI service quality is described in Clause 7.
Cybersecurity is also an important aspect that is captured in these SQuaRE models. For example, security,
confidentiality, compliance, authenticity, resistance and recoverability are characteristics or sub-
characteristics described in this document.
ISO/IEC 25012 contains a model for data quality that is complementary to the model defined in this
document. ISO/IEC 25012:2008 is being extended for AI systems by the ISO/IEC 5259 series.

© ISO/IEC 2025 – All rights reserved
v
DRAFT International Standard ISO/IEC DIS 25059:2025(en)
Software engineering — Systems and software Quality
Requirements and Evaluation (SQuaRE) — Quality models for
AI systems
1 Scope
This document outlines quality models for AI systems and services and is an application specific extension
to the standards on SQuaRE. The characteristics and sub-characteristics detailed in the models provide
consistent terminology for specifying, measuring and evaluating AI system and service quality. The
characteristics and sub-characteristics detailed in the models also provide a set of quality characteristics
against which stated quality requirements can be compared for completeness.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
ISO/IEC 25010:2023, Systems and software engineering — Systems and software Quality Requirements and
Evaluation (SQuaRE) — Product quality model
ISO/IEC 25019:2023, Systems and software engineering — Systems and software Quality Requirements and
Evaluation (SQuaRE) — Quality-in-use model
ISO/IEC 22989:2022, Information technology — Artificial intelligence — Artificial intelligence concepts and
terminology
ISO/IEC 23053:2022, Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 22989:2022,
ISO/IEC 23053:2022 and the following apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1 General
3.1.1
measure,noun
variable to which a value is assigned as the result of measurement
Note 1 to entry: The term “measure” is used to refer collectively to base measures, derived measures, and indicators.
[SOURCE: ISO/IEC/IEEE 15939:2017, 3.15]

© ISO/IEC 2025 – All rights reserved
ISO/IEC DIS 25059:2025(en)
3.1.2
measure,verb
make a measurement
[SOURCE: ISO/IEC/IEEE 15939:2017, 3.16]
3.1.3
software quality measure
measure of internal software quality, external software quality or software quality in use
Note 1 to entry: Internal measure of software quality, external measure of software quality or software quality in use
measure are described in the quality model in ISO/IEC 25010.
[SOURCE: ISO/IEC 25040:2024, 4.61]
3.1.4
risk treatment measure
protective measure
action or means to eliminate hazards or reduce risks
[SOURCE: ISO/IEC Guide 51:2014, 3.13, modified — change reduction to treatment.]
3.1.5
transparency
degree to which appropriate information about the AI system is communicated to relevant stakeholders
Note 1 to entry: Appropriate information for AI system transparency can include aspects such as features, components,
procedures, measures, design goals, design choices and assumptions.
3.1.6
environment
surroundings in which an organization operates, including air, water, land, natural resources, flora, fauna,
humans and their interrelationships
Note 1 to entry: Surroundings in this context extend from within an organization to the global system.
[SOURCE: ISO 14001:2015, 3.2.1, modified — Note 1 to entry was modified and Note 2 to entry was removed.]
3.1.7
sustainability aspect
element of an organization’s activities or products or services that can interact with the environment (3.1.6)
[SOURCE: ISO 14001:2015, 3.2.2, modified — The notes to entry were removed and new note 1 to entry
was added.]
3.1.8
environmental impact
any change to the environment (3.1.6), whether adverse or beneficial, wholly or partially resulting from an
organization’s environmental aspects (3.1.7)
[SOURCE: ISO 14001:2015, 3.2.3, modified]
3.1.9
life cycle assessment
LCA
systematic evaluation of the environmental impact (3.1.8) product(s) that include all stages of its life cycle
EXAMPLE Period from installation to uninstalling an AI system.
[SOURCE: ISO 17889-1:2021, 3.1.4, modified — updated EXAMPLE to refer to an AI system.]

© ISO/IEC 2025 – All rights reserved
ISO/IEC DIS 25059:2025(en)
3.1.10
economic sustainability
ability to provide sustainable, successful places in an economic context
Note 1 to entry: Economic considerations include employment, competitiveness, wealth and distribution, welfare,
accounting and regulation.
[SOURCE: ISO 17889-1:2021, 3.1.2]
3.1.11
social sustainability
ability to provide sustainable, successful places in a social context
Note 1 to entry: Social sustainability combines design of the physical realm with design of the world, infrastructure to
support social and cultural life, provides social amenities, systems for citizen engagement and spaces for people and
places to evolve.
[SOURCE: ISO 17889-1:2021, 3.1.3]
3.1.12
control,verb
in engineering, the monitoring of system output to compare with expected output and
taking corrective action when the actual output does not match the expected output
Note 1 to entry: Social sustainability combines design of the physical realm with design of the world, infrastructure to
support social and cultural life, provides social amenities, systems for citizen engagement and spaces for people and
places to evolve.
[SOURCE: ISO/IEC/IEEE 24765:2017, 3.846.1]
3.1.13
controller
authorized human or another external agent that performs a control
Note 1 to entry: A controller interacts with the control points of an AI system.
3.1.14
engagement of control
control engagement
process where a controller (3.1.13) takes over a set of control points (3.1.15)
Note 1 to entry: Besides taking over a set of control points, an engagement of control can also include a confirmation
about the transfer of control to a controller.
3.1.15
control point
part of the interface of a system where controls can be applied
Note 1 to entry: A control point can be a function, physical facility (such as a switch) or a signal receiving subsystem.
3.1.16
selection bias
type of data bias that can occur when a dataset’s samples are not collected in a way that is representative of
their real-world distribution
[SOURCE: ISO/IEC TS 12791:2024, 3.3.8]
3.2 Product quality
3.2.1
user controllability
degree to which a user can appropriately intervene in an AI system’s functioning in a timely manner

© ISO/IEC 2025 – All rights reserved
ISO/IEC DIS 25059:2025(en)
3.2.2
functional adaptability
degree to which an AI system can accurately acquire information from data, or the result of previous actions,
and use that information in future predictions
3.2.3
functional correctness
degree to which a product or system provides the correct results with the needed degree of precision
Note 1 to entry: AI systems, and particularly those using machine learning models, do not usually provide functional
correctness in all observed circumstances.
[SOURCE: ISO/IEC 25010:2023, 4.2.1.2, modified — Note to entry replaced.]
3.2.4
intervenability
degree to which an operator can intervene in an AI system’s functioning in a timely manner to prevent harm
or hazard
3.2.5
robustness
degree to which an AI system can maintain its level of functional correctness under any circumstances
3.2.6
environmental sustainability
state in which the ecosystem and its functions are maintained for the present and future generations
[SOURCE: ISO 17889-1:2021, 3.1.1, modified — generation made plural.]
3.3 Quality-in-use
3.3.1
environmental risk mitigation
degree to which a product or system mitigates the potential risk to the environment in the intended
contexts of use
Note 1 to entry: environmental risk mitigation includes the mitigation of risks that could harm the environment, such
as pollution, waste, and resource depletion. Additionally, it involves the product or system’s role in sustainability
through the efficient utilization of resources, reduction of waste, and facilitation of recycling and reuse processes.
3.3.2
societal and ethical risk mitigation
degree to which a product or system mitigates the potential societal or ethical risks
Note 1 to entry: Societal and ethical risk mitigation includes accountability, fairness, transparency and explainability,
professional responsibility, promotion of human value, privacy, human control of technology, community involvement
and development, respect for the rule of law, respect for international norms of behaviour and labour practices.
3.4 AI service quality
3.4.1
traceability
degree to which the AI service outcomes can be traced to or from the user needs
3.4.2
service adaptability
degree to which a service can be adapted based on factors such as functions, responsibility, skill of
employees, communication style with users or instruments available
3.4.3
customizability
degree to which the AI service can be customized at the request of users

© ISO/IEC 2025 – All rights reserved
ISO/IEC DIS 25059:2025(en)
4 Abbreviated terms
AI artificial intelligence
ML machine learning
5 Product quality model
5.1 General
An AI system product quality model is detailed in Table 1. The model is based on a modified version of a
general system model provided in ISO/IEC 25010. New and modified sub-characteristics are identified
using a lettered footnote. Some of the sub-characteristics have different meanings or contexts as compared
to the ISO/IEC 25010 model. The modifications, additions and differences are described in this clause.
The unmodified original characteristics are part of the AI system product model and shall be interpreted
in accordance with ISO/IEC 25010. Each of these modified or added sub-characteristics are listed in the
remainder of this clause.
Table 1 — AI system product quality model
Functional suitability Security
Functional completeness        Confidentiality
m
Functional correctness        Integrity
Functional appropriateness        Non-repudiation
a
Functional adaptability        Accountability
Performance efficiency        Authenticity
Time behaviour        Resistance
a
Resource utilization        Intervenability
Capacity Maintainability
a
Environmental sustainability          Modularity
Interaction capability         Reusability
Appropriateness recognisability         Analysability
Learnability         Modifiability
Operability Flexibility
User error protection        Testability
User engagement        Adaptability
Inclusivity        Scalability
User assistance        Installability
Self descriptiveness        Replaceability
a
User controllability Safety
a
Transparency        Operational constraint
Reliability       Risk identification
Faultlessness       Fail safe
Availability       Hazard warning
Fault tolerance       Safe integration
Recoverability Compatibility
© ISO/IEC 2025 – All rights reserved
ISO/IEC DIS 25059:2025(en)
TTabablele 1 1 ((ccoonnttiinnueuedd))
a
Robustness         Co-existence
Interoperability
NOTE
a
Added sub-characteristics.
m
Modified sub-characteristics.
5.2 User controllability
User controllability is a new sub-characteristic of interaction capability. User controllability is a property
of an AI system such that a controller can intervene in its functioning in a timely manner. Engagement of
control is important if unexpected behaviour cannot be completely avoided and intervention is required to
handle negative consequences.
User controllability is related to controllability, which is defined in ISO/IEC 22989:2022, 5.15.5 and described
in ISO/IEC TS 8200.
5.3 Functional adaptability
Functional adaptability is a new sub-characteristic of functional suitability. Functional adaptability of an
AI system is the ability of the system to adapt itself to a changing dynamic environment it is deployed in.
AI systems can learn from new training data, production data and the results of previous actions taken
by the system. The concept of functional adaptability subsumes that of continuous learning, as defined in
ISO/IEC 22989:2022, 5.11.9.2.
Continuous learning is not a mandatory requirement for functional adaptability. For example, a system
that switches classification models based on events in its environment can also be considered functionally
adaptive.
Functional adaptability in AI systems is unlike other quality characteristics as there are system specific
consequences that cannot be interpreted using a straight-line linear scale (e.g. bad to good). Generally,
higher functional adaptability can result in improvements for the outcomes enacted by AI systems.
For some systems, high functional adaptability can cause additional unhelpful outcomes to become more
likely based on the system’s previous choices. Weighti
...