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Quality Assessment and Assurance of Machine Learning Systems: A Comprehensive Approach

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Information and Communication Technologies in Education, Research, and Industrial Applications (ICTERI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1980))

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Abstract

Machine learning (ML) is opening up new opportunities for the development of innovative systems across a wide range of industries. However, assessing and ensuring the quality of systems with ML components introduces unique challenges related to inherent characteristics of such components like data centricity and unpredictable behavior. Traditional software quality assessment and assurance methods may not be sufficient for ML systems: (1) they focus on software code, while ML systems’ quality is influenced by the characteristics of the data and the algorithms used to create ML components; (2) they do not cover the emerging quality characteristics specific to ML systems, such as interpretability, explainability, fairness and trustworthiness. This PhD project aims to develop a comprehensive approach for assessing and assuring the quality of ML systems, with a focus on bias detection and prevention. The research will (1) explore the problem of bias in production ML systems; (2) analyze the gaps in existing software quality models and methods related to bias detection and prevention; and (3) propose an improved approach to quality assessment and assurance to address the challenges associated with bias in ML systems. The results of this PhD project are expected to contribute to the development of better models and methods for assessing and assuring the quality of ML systems, as well as have practical implications for industries that rely on ML systems to automate complex tasks, facilitate decision-making processes and gain insights from large amounts of data.

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Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, Ukraine

    Yurii Sholomii & Vitaliy Yakovyna

Authors
  1. Yurii Sholomii
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  2. Vitaliy Yakovyna
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Correspondence to Yurii Sholomii .

Editor information

Editors and Affiliations

  1. University of Huddersfield, Huddersfield, UK

    Grigoris Antoniou

  2. Ukrainian Catholic University, Lviv, Ukraine

    Vadim Ermolayev

  3. Kherson State University, Kherson, Ukraine

    Vitaliy Kobets

  4. Odessa National Polytechnic University, Odesa, Ukraine

    Vira Liubchenko

  5. University of Klagenfurt, Klagenfurt, Austria

    Heinrich C. Mayr

  6. Kherson State University, Kherson, Ukraine

    Aleksander Spivakovsky

  7. University of Warmia and Mazury in Olsztyn, Olsztyn, Poland

    Vitaliy Yakovyna

  8. V. N. Karazin Kharkiv National University, Kharkiv, Ukraine

    Grygoriy Zholtkevych

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Sholomii, Y., Yakovyna, V. (2023). Quality Assessment and Assurance of Machine Learning Systems: A Comprehensive Approach. In: Antoniou, G., et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2023. Communications in Computer and Information Science, vol 1980. Springer, Cham. https://doi.org/10.1007/978-3-031-48325-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-48325-7_20

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  • Online ISBN: 978-3-031-48325-7

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