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On the Different Concepts and Taxonomies of eXplainable Artificial Intelligence

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Intelligent Systems and Pattern Recognition (ISPR 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1941))

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Abstract

Presently, Artificial Intelligence (AI) has seen a significant shift in focus towards the design and development of interpretable or explainable intelligent systems. This shift was boosted by the fact that AI and especially the Machine Learning (ML) field models are, currently, more complex to understand due to the large amount of the treated data. However, the interchangeable misuse of XAI concepts mainly “interpretability” and “explainability” was a hindrance to the establishment of common grounds for them. Hence, given the importance of this domain, we present an overview on XAI, in this paper, in which we focus on clarifying its misused concepts. We also present the interpretability levels, some taxonomies of the literature on XAI techniques as well as some recent XAI applications.

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Acknowledgements

This research work was supported and funded by Data4Transport, a Tunisian-South Korean research project. The authors would like to thank all personnel involved in this work.

Author information

Authors and Affiliations

  1. Higher Institute of Management of Tunis, University of Tunis, Tunis, Tunisia

    Arwa Kochkach

  2. Faculty of Economics and Management of Nabeul, University of Carthage, Tunis, Tunisia

    Saoussen Belhadj Kacem & Sabeur Elkosantini

  3. Colleage of Engineering, United Arab Emirates University, Al Ain, United Arab Emirates

    Seongkwan M. Lee

  4. Hanyang University, ERICA Campus, Seoul, South Korea

    Wonho Suh

Authors
  1. Arwa Kochkach
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  2. Saoussen Belhadj Kacem
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  3. Sabeur Elkosantini
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  4. Seongkwan M. Lee
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  5. Wonho Suh
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Corresponding author

Correspondence to Arwa Kochkach .

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Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Akram Bennour

  2. Sharjah University, Sharjah, United Arab Emirates

    Ahmed Bouridane

  3. University of Toulouse, Toulouse, France

    Lotfi Chaari

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Kochkach, A., Kacem, S.B., Elkosantini, S., Lee, S.M., Suh, W. (2024). On the Different Concepts and Taxonomies of eXplainable Artificial Intelligence. In: Bennour, A., Bouridane, A., Chaari, L. (eds) Intelligent Systems and Pattern Recognition. ISPR 2023. Communications in Computer and Information Science, vol 1941. Springer, Cham. https://doi.org/10.1007/978-3-031-46338-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-46338-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46337-2

  • Online ISBN: 978-3-031-46338-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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