## 2 Explanation Methods  
[PERSON] et al. [20] summarized the various aspects through which explanation methods can be categorized. Figure 1 visualizes these aspects: data type, agnosticity, scope, and explanation purpose. Depending on the data that needs to be explained, explanation methods can be graph-based, image-based, text-based, and tabular-based, while the agnosticity indicates if the explanation method is applicable to explain any type of ML model or if it can explain only a specific type. Some of the explanation methods are local explanations because they are used to explain individual predictions instead of the whole model. The explanation methods can also be classified regarding the purpose of explanation into intrinsic, post hoc, enhanced model fairness, and test predictions sensitivity. Intrinsic-based explanation methods create interpretable models instead of complex models, while post hoc-based methods attempt to interpret complex models instead of using interpretable models. Although fairness is a surprisingly new field of ML explanation [20], the development made in the closing few years was not trivial. Several explanation methods have been produced to improve the fairness of ML models and guarantee the fair allocation of resources. One of the purposes of the explanation methods is to test the sensitivity of model prediction. Several explanation methods try to evaluate and challenge ML models to support the trustworthiness and reliability of predictions of these models. These methods employ a composition used to analyze the sensitivity, where models are examined  
Figure 1: Taxonomy of explanation methods. in terms of how sensitive their predictions are regarding the fine fluctuations in the corresponding inputs. The taxonomy in Figure 1 focuses on the purpose for which these methods were created and the approaches they use to accomplish that purpose. In accordance with the taxonomy, four main categories for explanation methods can be distinguished [20]: methods to interpret complex black-box models, methods to generate white-box models, methods to ensure fairness and restrict the existence of discrimination, and, finally, methods to analyze the model prediction sensitivity. According to [20], methods to interpret complex black-box models can be categorized into methods to interpret DL models and methods to interpret any black-box models. Methods to analyze the model prediction sensitivity can also be classified as traditional analysis methods and adversarial example-based analysis methods. The LIME, SHAP, Anchor, and LORE explanation methods, the models we employ in our work, belong to the methods to explain any black-box models. [PERSON] et al. [12] proposed the LIME explanation framework that can interpret the outputs of any classification model in an explainable and trusted way. This method relies on learning an explainable model in a local manner around the model output. They proposed an extension framework called (SP-LIME) to interpret models globally by providing representative explanations of individual outputs in a non-redundant manner. The proposed frameworks were evaluated with simulated and human subjects by measuring the impact of explanations on trust and associated tasks. The authors also showed how understanding the predictions of a neural network on images helps practitioners know when and why they should not trust a model. [PERSON] and [PERSON] [13] proposed the SHAP method to explain the ML model, where the importance of the input features is measured by the Shapley values. These values are inspired by the field of cooperative game theory, which aims to quantify each player's contribution to the game. They arise in situations where the number of players (features of input data) collaborates to acquire a reward (prediction of ML model). The proposed approach was evaluated by applying kernel SHAP and Deep SHAP approximations on the CNN's DL model used to classify digital images from the MNIST dataset. In another work, [PERSON] et al.