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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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.
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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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