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The combination of symbolic and sub-symbolic Artificial Intelligence (AI) provides an excellent opportunity for innovations that merge the interpretability of the former with the learning capabilities of the latter. This paper presents Fuzzy Cognitive Maps (FCMs) as a hybrid and flexible model that combines the strengths of both paradigms and proposes them as a feasible solution to the challenges of explainability and interpretability in AI systems without losing working feasibility. FCMs have emerged as a robust framework for representing causal knowledge and facilitating intuitive and justifiable decision-making processes, but there is much more to explore. FCMs can handle the inherent uncertainty and vagueness present in real-world scenarios, allowing for a more natural approach to problem-solving in combination with the learning and adaptation capabilities of sub-symbolic AI. FCMs are an ideal choice for applications requiring high levels of explainability and interpretability.
Maikel León (Wed,) studied this question.