Oleksandr Gordieiev

This paper addresses the growing need for reliable methods to evaluate the quality of artificial intelligence (AI) systems as they become widely used in both critical domains and everyday applications. The study aims to develop a metric-based approach to assessing AI system quality by harmonising product quality and quality in use models in line with updated international standards. To achieve this, the authors analyse existing ISO/IEC 25000 series standards, identifying inconsistencies between older and newer versions, and propose an updated quality model that incorporates both perspectives. Building on guidance documents, international standards, and contemporary research, the study introduces a set of metrics designed to measure new subcharacteristics of AI system quality, particularly where standardised metrics have not yet been developed. The proposed approach bridges the gap between established quality models (ISO/IEC 25010:2023, ISO/IEC 25019:2023, ISO/IEC 25059:2023) and standardised measurement practices (ISO/IEC 25023:2016, ISO/IEC 25022:2016), enabling more consistent and practical evaluation of AI systems. These metrics can be applied by researchers and practitioners to improve the quality of AI systems, enhance their reliability, and reduce risks associated with insufficient quality. Future work will focus on empirical validation of the proposed approach to confirm its applicability and usefulness across diverse AI applications.