# Fuzzy Cognitive Maps: Their Role in Explainable Artificial Intelligence
## 1 Introduction  
Advanced artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) [1], is a dominant factor in emerging technologies and traditional social and economic sciences. Human society expects ML and DL to solve intricate problems, alleviate human interfness, and offer reliable projections [2]. The demand for transparent and explainable methods has grown since AI is becoming part of our lives, with its decisions affecting our everyday activities [3].  
The response of the AI community to the above needs has catalyzed the research for explainable artificial intelligence (XAI) [4; 5] methods. However, XAI is a new discipline, established just recently in 2018 [6], though earlier significant research papers have pointed out the need for explainable models [7]. XAI refers not only to the technical aspects of the DL models that ensure some level of interpretability but also integrates the concepts of data privacy and accountability [8].  
Combining learning from data with human knowledge drawn from the expertise and scientific training of those who develop artificial intelligence systems is a promising approach. Such an adhesive approach may be the fuzzy cognitive map (FCM) approach [9; 10; 11]. FCMs are the elements and methods union of fuzzy logic and neural networks (NNs). It is asoft computational method of modelling fuzzy and unstable conditions. An FCM describes a system with a graphic display that includes concepts and their relationships. They intend to model human knowledge, not discover it from raw data. FCMs belong to traditional AI methods, but their evolvement makes them suitable candidates for solving complex problems, which ML and DL dominate.  
FCMs' nature makes them interpretable and explainable [12]. In the present paper, we defend FCMs and their suitability for presenting explainable decisions and working with advanced ML and DL frameworks.  
To the best of our knowledge, this research is the first to discuss the aspects and applications of FCMs under the XAI perspective. Recent literature provides insights into the functionalities of FCMs and their applicability in various domains. At the same time, much discussion has been held on the concepts of XAI and how they are taken care of in AI-based solutions. The relationship between FCMs and XAI is not well-documented and demonstrated yet.  
The paper also aims to introduce essential FCM aspects to practitioners and newcomers to the field of artificial intelligence and to discuss the explainability issue through examples. The contributions of this research can be summarized as follows:  
* The nature and functions of FCMs are thoroughly analyzed from the point of view of explainable artificial intelligence and its properties;
* Key literature is presented to demonstrate the significance of FCMs, both in terms of their performance and their interpretability;
* The results demonstrate that FCMs are both in accordance with the XAI directives and have many successful applications in domains such as medical decision-support systems, energy savings, precision agriculture, environmental monitoring, and policy-making for the public sector;
* The authors argue that the fields of ML, DL, and FCMs can be combined to develop robust AI solutions that are explainable and able to handle multiple sources of data, including human knowledge.  
Critical studies deploying FCMs are briefly discussed to support the topic. Section 2 briefly discusses the background theory of FCMs. New practitioners will be informed about the main aspects of AI methods focused on the learning process and knowledge extraction or encapsulation. Critical XAI perspectives of FCMs are presented in Section 3. The reader will understand the inherent explainable nature of FCMs and their limitations in contrast with learning methods. The study discusses the potential cooperation of diverse practices in ensemble systems. Such systems merge the exciting learning opportunities of networks with the causality-oriented operations of FCMs. Conclusions are drawn in Section 4.