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Research paper

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,

Kuwajima, Hiroshi, Ishikawa, Fuyuki
arXiv.org perpetual, non-exclusive licenseView resource
Research paper

Additional file 1 of Detection of hidden earthquakes after the 2011 Tohoku earthquake by automatic hypocenter determination combined with machine learning

Additional file 1: Figure S1. Changes in earthquake detectability in and around eastern Japan (≤ 50 km depth). Figure S2. Hypocentral distribution of events in the JMA unified catalog during the 11 years preceding the 2011 Tohoku earthquake. Figure S3. Heat map of events in the JMA unified catalog during the 11 years preceding the 2011 Tohoku earthquake. Table S1. Features used in our LightGBM quality control classifier.

Tamaribuchi, Koji, Kudo, Shota, Shimojo, Kengo, Hirose, Fuyuki
Creative Commons Attribution 4.0 InternationalView resource

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