Stepin, IliaSuffian, MuhammadCatalá Bolós, AlejandroAlonso Moral, José María2025-07-222025-07-222024I. Stepin, M. Suffian, A. Catala and J. M. Alonso-Moral, "How to Build Self-Explaining Fuzzy Systems: From Interpretability to Explainability [AI-eXplained]," in IEEE Computational Intelligence Magazine, vol. 19, no. 1, pp. 81-82, Feb. 2024, doi: 10.1109/MCI.2023.33280981556-603Xhttps://hdl.handle.net/10347/42565Fuzzy systems are known to provide not only accurate but also interpretable predictions. However, their explainability may be undermined if non-semantically grounded linguistic terms are used. Additional non-trivial challenges would arise if a prediction were to be explained counterfactually, i.e., in terms of hypothetical, non-predicted outputs. In this paper, we explore how both factual and counterfactual automated explanations can justify the output of fuzzy rule-based classifiers, and thus contribute to making them more trustworthy. Moreover, we demonstrate how end user preferences can be handled by customizing automated explanations, making them interactive, personalized, and therefore, human-centric. The full immersive article at IEEE Xplore provides detailed interactive examples for better understanding.eng© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.journal article10.1109/MCI.2023.33280981556-6048open access