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Explainable AI and Fuzzy Logic Systems

  • Ravikiran Chimatapu
  • Hani Hagras
  • Andrew Starkey
  • Gilbert Owusu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

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.

Keywords

Explainable AI XAI Deep fuzzy systems Fuzzy logic systems 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ravikiran Chimatapu
    • 1
  • Hani Hagras
    • 1
  • Andrew Starkey
    • 2
  • Gilbert Owusu
    • 2
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK
  2. 2.Business Modelling and Operational Transformation PracticeBritish TelecomIpswichUK

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