Critical Thinking About Explainable AI (XAI) for Rule-Based Fuzzy Systems
Jerry M. Mendel, Piero P. Bonissone · IEEE Transactions on Fuzzy Systems · 2021
This article is about explainable artificial intelligence (XAI) for rule-based fuzzy systems [that can be expressed generically, as$y({{\bf x}}) = f({{\bf x}})$]. It explains why it isnot validto explain the output of Mamdani or Takagi–Sugeno–Kang rule-based fuzzy systems using IF-THEN rules, and why itis validto explain the output of such rule-based fuzzy systems as anassociationof the compound antecedents of a small subset of the original larger set of rules, using a phrase such as “these linguistic antecedents aresymptomaticof this output.” Importantly, it provides a novel multi-step approach to obtain such a small subset of rules for three kinds of fuzzy systems, and illustrates it by means of a very comprehensive example. It also explains why the choice for antecedent membership function shapes may be more critical for XAI than before XAI, why linguistic approximation and similarity are essential for XAI, and, it provides a way to estimate the quality of the explanations.