Selecting the Most Adequate Fuzzy Operation for Explainable AI: Empirical Fact and Its Possible Theoretical Explanation

Authors

Orsolya Csiszar, Aalen University of Applied SciencesFollow
Gábor Csiszar, University of StuttgartFollow
Martine Ceberio, The University of Texas at El PasoFollow
Vladik Kreinovich, The University of Texas at El PasoFollow

Publication Date

6-1-2023

Comments

Technical Report: UTEP-CS-23-24

Abstract

A reasonable way to make AI results explainable is to approximate the corresponding deep-learning-generated function by a simple expression formed by fuzzy operations. Experiments on real data show that out of all easy-to-compute fuzzy operations, the best approximation is attained if we use an operation a + b − 0.5 ( limited to the interval [0,1]$. In this paper, we provide a possible theoretical explanation for this empirical result.