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
Using ISO/IEC 25059:2023 helps organizations create clearer, measurable and ethically aware quality requirements for AI systems, improving evaluation, procurement and oversight.
Open preview (PDF)Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems
