# Fuzzy Cognitive Maps: Their Role in Explainable Artificial Intelligence
## 5 Discussion  
New regulations in the European Union have proposed that people affected by algorithmic decisions have a right to an explanation [45]. This drives the research towards explainable AI forward. ML engineers put effort into developing explainability methods to fight against the black-box nature of most AI methods. The demand for transparent and explainable methods has grown since AI is becoming part of our lives, and its decisions affect our everyday activities. In medicine, for example, ML and DL decisions affect human lives directly unless supervised by human experts. In this context, the opaqueness of the new models, also called black boxes [49], i.e., their inherent inability to explain their decisions, makes the medical experts reticent to adopt these models. At the same time, FCMs offer a robust solution for complex decision-making problems. The present study has discussed the benefits of FCMs regarding their role in XAI. FCMs are a powerful tool for understanding the relationships between variables or concepts in a system, allowing for the analysis of system behaviour in a simple and symbolic way. They are unique in their ability to address the problem of causation in any physical or human-made system. Unlike statistical models, FCMs do not calculate statistical values but, rather, use membership functions to describe the relationships between variables. In most domains, it is important to distinguish between correlation and causation, and FCMs can be a valuable tool for exploring causal relationships between variables. FCMs are generally considered highly interpretable because they represent the system using simple and intuitive concepts, such as nodes and edges. However, the interpretability of FCMs can be affected by the complexity of the modelled system and the number of nodes and edges in the model. FCMs are also transparent because their internal structure can be easily visualized and analyzed. FCMs can be informative because they can identify key factors that influence the system and simulate different scenarios and their potential outcomes. However, the informativeness of FCMs can be affected by the quality of the data used to build the model and the accuracy of the model's parameters. FCMs can be transferable because they can be adapted and applied to different systems by changing the nodes and edges in the model. However, the transferability of FCMs can be affected by the degree of similarity between the systems being modelled and the availability of data and knowledge. FCMs can be robust because they can tolerate some uncertainty and imprecision in the input data. However, the robustness of FCMs can be affected by the degree of uncertainty and the accuracy of the model's parameters. FCMs can be used to model causality by representing the strength and direction of the relationships between nodes in the model. However, it is important to note that FCMs only model correlation, not causation. Table 5 summarizes recent FCM models in various domains. The presented models meet the demands of XAI models. In particular, each research study is discussed based on each uniqueness regarding the model trust, transferability, causality, and transparency. FCMs have limitations. Firstly, there are limits to the number of attributes they can represent. FCMs are unable to cope with the dynamic nature of real-world problems. As a result, they cannot adequately represent the complex relationships between multiple variables. Second, they can be computationally intensive. The operation of FCMs requires considerable processing power and can be slow and difficult to solve. This makes them difficult to use in real-time applications. Third, they may not be sufficiently precise. FCMs lack the accuracy of other AI techniques, such as NNs, and their results may not be as accurate. Fourth, FCMs are vulnerable to errors in their knowledge base. If the knowledge base is wrong or outdated, it can lead to incorrect results. Finally, they lack the flexibility of other AI techniques.