Abstract
Advancements that merge the clarity of symbolic AI with the adaptive learning traits of subsymbolic AI show great potential at the intersection of these two AI forms. This study introduces Fuzzy Cognitive Maps (FCMs). This hybrid model integrates the optimal characteristics of both frameworks to address the challenges of interpretability and explainability in artificial intelligence (AI) systems. FCMs provide a robust framework for logically and intuitively supporting decision-making processes and representing causal relationships. Their capacity to handle the inherent vagueness and uncertainty of real-world scenarios enables a more natural and flexible approach to problem-solving. Due to their intrinsic adaptability and learning capabilities derived from sub-symbolic AI, FCMs are particularly suited for applications demanding high levels of interpretability and explainability.