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Fuzzy Systems Interpretability: What, Why and How

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Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 394))

Abstract

Interpretability has been always present in Machine Learning and Artificial Intelligence. However, it is difficult to measure it (even to define it), and quite commonly it collides with other properties as accuracy, with a clear meaning and well defined metrics. This situation has reduced its influence in the area. But due to different external reasons, interpretability is now gaining importance in Artificial Intelligence, and particularly in Machine Learning. This new situation has two effects on the field of fuzzy systems. First, considering the capability of the fuzzy formalism to describe complex phenomena in terms that are quite close to human language, fuzzy systems have gained significant presence as an interpretable modeling tool. Second, the attention paid to interpretability of fuzzy systems, that grew during the first decade of this century and then experienced a certain decay, is growing again. The present paper will consider four questions regarding interpretability: what is, why is it important, how to measure it, and how to achieve it. These questions will be first introduced in the general framework of Artificial Intelligence, to be then focused from the point of view of fuzzy systems.

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Authors and Affiliations

  1. Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660, Boadilla del Monte, Madrid, Spain

    Luis Magdalena

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  1. Luis Magdalena
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Correspondence to Luis Magdalena .

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Editors and Affiliations

  1. Sorbonne Université, CNRS, LIP6, Paris, France

    Marie-Jeanne Lesot

  2. Sorbonne Université, CNRS, LIP6, Paris, France

    Christophe Marsala

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Magdalena, L. (2021). Fuzzy Systems Interpretability: What, Why and How. In: Lesot, MJ., Marsala, C. (eds) Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications. Studies in Fuzziness and Soft Computing, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-54341-9_10

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