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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Explainable AI and Fuzzy Logic Systems

verfasst von : Ravikiran Chimatapu, Hani Hagras, Andrew Starkey, Gilbert Owusu

Erschienen in: Theory and Practice of Natural Computing

Verlag: Springer International Publishing

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Abstract

The recent advances in computing power coupled with the rapid increases in the quantity of available data has led to a resurgence in the theory and applications of Artificial Intelligence (AI). However, the use of complex AI algorithms like Deep Learning, Random Forests, etc., could result in a lack of transparency to users which is termed as black/opaque box models. Thus, for AI to be trusted and widely used by governments and industries, there is a need for greater transparency through the creation of explainable AI (XAI) systems. In this paper, we introduce the concepts of XAI and give an overview of hybrid systems which employ fuzzy logic systems which can hold great promise for creating trusted and explainable AI systems.

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Nächstes Kapitel Computing Preimages and Ancestors in Reaction Systems
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Metadaten
Titel
Explainable AI and Fuzzy Logic Systems
verfasst von
Ravikiran Chimatapu
Hani Hagras
Andrew Starkey
Gilbert Owusu
Copyright-Jahr
2018
Buch
Theory and Practice of Natural Computing

Print ISBN: 978-3-030-04069-7

Electronic ISBN: 978-3-030-04070-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-04070-3_1

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