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On the Different Concepts and Taxonomies of eXplainable Artificial Intelligence
- Issue Date
- Nov-2023
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Explainability; EXplainable Artificial Intelligence; Interpretability; Post-hoc explanation techniques
- Citation
- Intelligent Systems and Pattern Recognition Third International Conference, ISPR 2023, Hammamet, Tunisia, May 11–13, 2023, Revised Selected Papers, Part II, pp 75 - 85
- Pages
- 11
- Indexed
- SCOPUS
- Journal Title
- Intelligent Systems and Pattern Recognition Third International Conference, ISPR 2023, Hammamet, Tunisia, May 11–13, 2023, Revised Selected Papers, Part II
- Start Page
- 75
- End Page
- 85
- 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. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Related Researcher
- Suh, Won ho
- ERICA 공학대학 (DEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING)