Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems

Understand

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.