Abstract
There is a lot of discussion regarding the interpretability and explainability of modern artificial intelligence methodologies, especially in applications such as medical imaging. Scientists argue that the most vital drawback of complex algorithms is their behaviour as black boxes. It is agreed that applying the newly developed methods in industry, medicine, agriculture, and other modern fields, such as the Internet of Things, requires the trustfulness of the systems from the users. Users are always entitled to know why and how each method made a decision and which factors played a role. Otherwise, they will always be wary of using new techniques. Fuzzy Cognitive Maps are an evolving computational method to model human knowledge, provide decisions handling uncertainty, and are the core of many modern intelligent systems. Numerous studies in various fields employ FCMs, which report top performance, sometimes proving superior to several Machine Learning models. In this work, we analyse the nature of FCMs in terms of their trust, transferability, causality, informativeness, and transparency, providing the reader with several success stories that reveal the suitability of FMCs in many domains.
