ISO/IEC 25059:2023 PDF

Name: ISO/IEC 25059:2023 — Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems
SKU: ISO/IEC 25059:2023
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Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems

Название (английский):

ISO/IEC 25059:2023

Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems

Статус документа:: Действующий
Формат:: Электронный (PDF)
Количество страниц:: 15
Дата публикации:: 28 июня 2023 г.
Издание:: ISO/IEC IS 25059 edition 1 version 1
ICS:: 35.080

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Описание (английский):

This document outlines a quality model for AI systems and is an application-specific extension to the standards on SQuaRE. The characteristics and sub-characteristics detailed in the model provide consistent terminology for specifying, measuring and evaluating AI system quality. The characteristics and sub-characteristics detailed in the model also provide a set of quality characteristics against which stated quality requirements can be compared for completeness.

Abstract

Похожие стандарты

Упомянутые в описании и другие стандарты ISO

Overview - ISO/IEC 25059:2023, Quality model for AI systems

ISO/IEC 25059:2023 is an application-specific extension to the SQuaRE family that defines a quality model for AI systems. Published by ISO/IEC JTC 1/SC 42, the standard provides consistent terminology and a structured set of characteristics and sub‑characteristics to help specify, measure and evaluate AI system quality. It complements ISO/IEC 25010 (SQuaRE) by addressing AI‑specific properties such as probabilistic behaviour, data dependence, continuous learning and human‑in‑the‑loop needs.

Key topics and technical requirements

Purpose: Provide a vocabulary and model for stating and comparing quality requirements for AI systems and for assessing completeness of requirements.

Product quality model (Clause 5): Extends ISO/IEC 25010 with AI‑specific characteristics and sub‑characteristics, including:

User controllability - ability of users to intervene in an AI system in a timely manner.
Functional adaptability - system’s capacity to acquire new information (including continuous learning) and use it for future predictions.
Functional correctness - degree to which results meet required precision, noting that AI systems may not guarantee correctness in all circumstances.
Robustness - ability to maintain functional correctness under varied conditions.
Transparency - degree to which appropriate information (features, design choices, assumptions) is communicated to stakeholders.
Intervenability - capability for operators to intervene to prevent harm or hazards.

Quality in use model (Clause 6):

Societal and ethical risk mitigation - measures addressing accountability, fairness, privacy, human control and other societal impacts.
Transparency in use - user‑facing and societal transparency requirements.

Measurement and evaluation: The model supports specifying measures and indicators (base and derived) for evaluation, without prescribing particular metrics.

Practical applications and target users

ISO/IEC 25059:2023 is intended for organizations and practitioners who design, develop, deploy or evaluate AI systems:

AI developers and architects - to define quality requirements and design choices (robustness, adaptability, transparency).

Quality assurance and test teams - to derive evaluation criteria and measurement plans aligned with SQuaRE concepts.

Risk managers and compliance officers - to map societal and ethical risk mitigation into measurable quality goals.

Procurement and auditors - to compare vendor claims against a standardized quality model.

Regulators and policy makers - to reference consistent terminology for AI system assessment.

Related standards

ISO/IEC 25010:2011 (SQuaRE system and software quality models)

ISO/IEC 22989:2022 (AI concepts and terminology)

ISO/IEC 23053:2022 (Framework for AI systems using ML)

ISO/IEC TR 24028 (trustworthiness of AI systems) and ISO/IEC 29119‑11 (testing of AI systems)

ISO/IEC 25012:2008 and the emerging ISO/IEC 5259 series (data quality for AI)

Using ISO/IEC 25059:2023 helps organizations create clearer, measurable and ethically aware quality requirements for AI systems, improving evaluation, procurement and oversight.

ISO/IEC 25059:2023 PDF

Abstract

Похожие стандарты

Overview - ISO/IEC 25059:2023, Quality model for AI systems

Key topics and technical requirements

Practical applications and target users

Related standards

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