Abstract
This special issue explores foundation methodologies, the latest directions, and emerging applications in a new generation of fuzzy systems related to human-explainable AI. The presented studies offer a snapshot of the latest advances in human-centric intelligence and interdisciplinary research, including computational behavioral science, learning methods for interpretable models, theoretical approaches and knowledge representation to explainability, decision support systems, and fuzzy control systems to explain robot behavior. They provided comprehensible explanations of AI decisions that enhanced the training performance on the benchmarks and the transparency through an interface to the end-users and engineers.
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