# Fuzzy Cognitive Maps: Their Role in Explainable Artificial Intelligence
## 4 Results  
### FCMs and Trust  
Trust is directly related to the model's effectiveness [4]. In classification tasks, measuring specific metrics (e.g., accuracy, precision, recall, F-1 score, and AUC score) determines the robustness of the developed model. The F-1 score is the harmonic mean of the model's precision and recall and is used when there is a class imbalance problem. The AUC score, or area under the curve score, is a metric used to measure the performance of a machine learning model. It measures the accuracy of a model by calculating the area under the curve of a receiver operating characteristic (ROC) curve. This AUC score is a value between 0 and 1, with 1 being the best model performance. AUC is a useful tool for comparing different models and their accuracy. In studies on object detection, image segmentation, and regression, appropriate metrics are used to measure the model's robustness. However, as a notion itself, trust does not have a specific meaning regarding AI. It is a simplification to identify the algorithm's accuracy with our confidence. Developing a reliable ML model typically involves the steps in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation of an ML Model in Terms of Quantifiable Performance Metrics** \\ \hline Step I: & Determine the desired outcome \\ Step II: & Select datasets \\ Step III: & Perform data pre-processing \\ Step IV: & Define the model \\ Step V: & Perform a proper k-fold cross-validation training-testing scheme \\ Step VI: & Obtained the trained model \\ Step VII: & Evaluate the model on external data \\ Step VIII: & Employ post-hoc explainability methods \\ Step IX: & Measure computational costs and times \\ Step X: & Evaluate the model’s consistency in producing the same results \\  & after separate training with the same data \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A typical procedure for assessing the ML model’s robustness. Fuzzy cognitive maps are typically not data-driven models but knowledge-driven models. Their object is to incorporate human expertise in problem-solving tasks. However, their effectiveness cannot be measured without using real-case examples, even if they are few. As long as we trust human knowledge, we trust that there is a suitable FCM to model it, even in very demanding challenges and changing environments. If we are sure of the authenticity of the scientific knowledge of those involved in creating the model, we are confident of the model itself, as long as it does, indeed, work. Trust is also related to our confidence in the model's technical creators. However, this topic applies to everything that man creates, and it makes no sense to specialize it further in FCMs. Fuzzy cognitive maps are potentially trustworthy because they incorporate human knowledge. In [28], the authors highlight the importance of the human-in-the-loop philosophy as a vital component of every AI method. That is the case with FCMs. In [22], the authors developed a state-space FCM to predict coronary artery disease from several clinical attributes, including symptoms, recurrent diseases, and diagnostic tests. The FCM model achieved an accuracy of 85.47% in discriminating healthy and diseased subjects from a dataset of 303 instances. Though this model did not contain any learning freedom, it succeeded in modelling human knowledge since the experts had defined the weights of each concept. The authors compared the model with ML algorithms, which performed worse and showed great under-fitting. In a similar study [19], the authors benchmarked more ML algorithms for the same task. Again, ML failed to learn from the input data, obtaining an accuracy of 10% less than the FCM in [22]. Trust is also related to the type of model evaluation and the associated data. Incomplete data is responsible for incomplete and biased models in ML and DL. There needs to be a very carefully designed validation process for assessing the model's ability to adapt to changing attributes and variations in data distributions.