Experimental Study on Generating Multi-modal Explanations of Black-box Classifiers in terms of Gray-box Classifiers
Authors:
- 1 July 2020
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-8
Abstract
Artificial Intelligence (AI) is a first class citizen in the cities of the 21st century. In addition, trust, fairness, accountability, transparency and ethical issues are considered as hot topics regarding AI-based systems under the umbrella of Explainable AI (XAI). In this paper we have conducted an experimental study with 15 datasets to validate the feasibility of using a pool of gray-box classifiers (i.e., decision trees and fuzzy rule-based classifiers) to automatically explain a black-box classifier (i.e., Random Forest). Reported results validate our approach. They confirm the complementarity and diversity among the gray-box classifiers under study, which are able to provide users with plausible multi-modal explanations of the considered black-box classifier for all given datasets.Keywords
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