## 5 Results
### Rule-Based Explanations
#### 5.3.5 Evaluation of Explainability  
The development of methodologies or definitions of metrics to evaluate the explanation generation techniques as well as to assess the quality of the generated explanations is comparatively lower than the extreme increase in research works devoted to exploring new methodologies of XAI. In this study, only nine articles among the selected articles were found to be fully intended for the evaluation OF and metrics for XAI. However, all the articles proposing new methods to add explainability considered one of the three techniques to assess their explainable model or the explanations generated by the models. These techniques were (i) user studies; (ii) synthetic experiments; and (iii) real experiments. The number of studies adopting each of the techniques Are illustrated in Figure 13. It was observed that most of the studies invoked user studies and synthetic experiments as standalone methods for evaluating the proposed explainable systems. Very few studies only used real experiments to evaluate their proposed systems. However, several studies conducted a combination of the user studies, real and synthetic experiments in the evaluation process as illustrated in the UpSet plot in Figure 13. User studies were mostly performed to evaluate the quality of the generated explanation in the form of case studies and questionnaire surveys. Generally, these cases are formulated by the researchers combining a real or synthetic scenario that is associated with some prediction/classification output and its explanation in any of the forms presented in Section 5.3.4. The surveys were observed to be conducted among the respective domain experts. They had to answer questions on the understandability and quality of the explanations from the presented case studies. To facilitate the user studies, [PERSON] et al. proposed the System Causability Scale (SCS) to measure the quality of explanations [56]. In simpler terms, the SCS resembles the widely known Likert scale [180]. In earlier work, [PERSON] and [PERSON] introduced the notion of the _cognitive value_ of an explanation and related its function in generating significant explanations within a given setting [62]. [PERSON] et al. proposed the methodology of a user study to measure the human-interpretability of logic-based explanations [125]. The prime metrics were the response time for understanding, the accuracy of understanding, and the subjective satisfaction of the users. [PERSON] et al. explicitly conducted a simulated user experiment to address the following questions [12]: (1) Aare the explanations faithful to the model? (2) Can the explanations aid users to ascertain trust in predictions? and (3) Are the explanations useful for evaluating the model as a whole? They also involved human subjects in evaluating the explanations generated by LIME and SP-LIME within the following situations [12]: (1) whether users can choose a better classifier in terms of generalisation; (2) whether the users can perform feature engineering to improve the model; and (3) whether the users are capable of pointing out the irregularities of a classifier by observing the explanations. \begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods for**} & \multirow{2}{*}{**References**} & \multicolumn{2}{c}{**Stage**} & \multicolumn{2}{c}{**Scope**} & \multicolumn{2}{c}{**Form**} & \multicolumn{2}{c}{**Models for Primary Task**} \\ \cline{3-10}  & & \(Ah\) & Ph & \(L\) & G & N & \(R\) & \(T\) & \(V\) & **Task** \\ \hline Template-Based Natural Language Generation & [164] & ✓ & & ✓ & & & & ✓ & & UM \\ Time-Varying Neighbourhood & [101] & & & ✓ & ✓ & & ✓ & & ✓ & NN \\ TreeExplainer & [84] & & & ✓ & ✓ & ✓ & ✓ & & ✓ & MA \\ TREPAN & [89] & & ✓ & ✓ & & & ✓ & & NN \\ Tripartite Graph & [162] & ✓ & & ✓ & & ✓ & & ✓ & UM \\ WM Algorithm & [157] & & ✓ & & ✓ & & ✓ & & FM \\ xDNN & [116] & ✓ & & & ✓ & & ✓ & & NN \\ XRAI & [147] & & ✓ & ✓ & & & & ✓ & NN \\ \hline \hline \end{tabular}
\end{table}