Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems
More and more software practitioners are tackling towards industrial applications of artificial intelligence (AI) systems, especially those based on machine learning (ML). However, many of existing principles and approaches to traditional systems do not work effectively for the system behavior obtained by training not by logical design. In addition, unique kinds of requirements are emerging such as fairness and explainability. To provide clear guidance to understand and tackle these difficulties, we present an analysis on what quality concepts we should evaluate for AI systems. We base our discussion on ISO/IEC 25000 series, known as SQuaRE,
Resource information
- Authors
- Kuwajima, Hiroshi, Ishikawa, Fuyuki
- Research area
- Artificial intelligence
- Research topics
- Computers and Society (cs.CY), Machine Learning (cs.LG), Software Engineering (cs.SE), FOS: Computer and information sciences, FOS: Computer and information sciences
- Publication date
- 2019
- DOI
- 10.48550/arxiv.1908.02134
- Upstream last modified
- Feb 26, 2022
- License
- arXiv.org perpetual, non-exclusive license
- Last record update
- Nov 4, 2026
About this resource
More and more software practitioners are tackling towards industrial applications of artificial intelligence (AI) systems, especially those based on machine learning (ML). However, many of existing principles and approaches to traditional systems do not work effectively for the system behavior obtained by training not by logical design. In addition, unique kinds of requirements are emerging such as fairness and explainability. To provide clear guidance to understand and tackle these difficulties, we present an analysis on what quality concepts we should evaluate for AI systems. We base our discussion on ISO/IEC 25000 series, known as SQuaRE, and identify how it should be adapted for the unique nature of ML and $\textit{Ethics guidelines for trustworthy AI}$ from European Commission. We thus provide holistic insights for quality of AI systems by incorporating the ML nature and AI ethics to the traditional software quality concepts.