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Explainable Artificial Intelligence (XAI) has experienced significant growth in recent years. This can be attributed to the increasing adoption of Machine Learning (ML) models, and the need to understand the reasons behind their inferences, as most of them are “black boxes”, lacking interpretability and explainability. To deal with this issue, the most promising approaches include Fuzzy Cognitive Maps (FCMs), which due to their simple yet effective structure to knowledge modeling they are applicable to a wide variety of scientific domains. Although several studies have reviewed the various modifications and applications of FCMs, the potential of the FCMs in terms of interpretable/explainable decision-making has not been sufficiently explored. This review study considers the capacity of FCMs in the context of transparent, interpretable, and explainable ML, with four main contributions: a) it systematically reviews the most recent developments in FCMs, that have been proposed between 2018 and 2024, as well as their prospects in XAI; b) it presents recently proposed learning algorithms, used to train the FCMs; c) it reports the latest applications of FCMs, including state-of-the-art models for interpretable 1D signal and image analysis; d) it identifies limitations of the current FCM models and highlights open challenges and perspectives, indicating novel pathways for innovation towards XAI.
Sovatzidi et al. (Sat,) studied this question.