## 6 Discussion
The selected articles were analysed from five different viewpoints, i.e., metadata, primary task, explainability, the form of explanation, and the evaluation of methods and explanations. The prominent features from the respective viewpoints are summarised in Table 3. The features and possible alternatives were set in such a way that the result of the analysis can substantially address the RQs. Section 5 presents the outcomes of the analysis by identifying insights into the domains and applications in which XAI is developing, the prevailing methods of generating and evaluating explanations, etc. This information is thus readily available for prospective researchers from miscellaneous domains to instigate research projects on the methodological development of XAI. In addition, a questionnaire survey was designed and administered to the authors of the selected articles with several aims: to cure the extracted feature values from the articles, to assess the credibility of the definition of the features, etc. The questionnaire was distributed to the authors through email, and the response rate was approximately 50%. The responses were apparently similar to the information extracted from the articles, except in a few cases. For example, from the article, it was found that the input data for the method developed by [PERSON] were numeric [103]. In contrast, from the author's response, the input data were mentioned as LSP, and this information was incorporated in the analysis. This instance of curating, clarifying, and cross-checking the information extracted from the articles advocates the need for a questionnaire survey. This review study took advantage of the questionnaire survey to assess the credibility of the literature reviewer as well as clarify the information. During the exploration of the contents of the sorted-out articles, the first step was to analyse the metadata. To determine the relevancy of the articles, keywords that were explicitly defined by the authors and keywords extracted from the abstracts were investigated in the form of word clouds following the methodology developed by [PERSON] et al. [44]. It was observed that the significant terms were _explainable artificial intelligence_, _deep learning_, _machine learning_, _explainability_, _visualisation_ etc. These terms were considered significant due to their larger appearance in the word cloud, which resulted from repeated occurrences of the terms in the supplied texts. In addition, a higher number of occurrences of terms, such as _deep learning_ or _visualisation_, aligns with the higher number of studies with concepts presented in Tables 5 and 6, indicating tunnel vision in XAI development. More attention towards less investigated models, such as SVM and neuro-fuzzy models and visualisation techniques would add more value and novelty towards XAI. Moreover, the prominent terms are strongly related to the primary concept of this study, which increases the confidence in the selected articles that they are related. In addition, the terms from the author-defined keywords were more conceptual than the terms from the abstracts of the articles. On the other hand, the abstracts contained more specific terms based on the application tasks and AI/ML models. From the metadata, the countries of the authors' affiliations were evaluated, and it was found that the USA leads by a significant margin in terms of the number of publications. However, the collective publications from the countries belonging to the EU exceed the number of publications from the USA. This high number of publications indicated the immense impact of imposing various regulations and expressing interest through different programs from different governments. Although there was a development plan on XAI from the Government of China, the number of screened articles was lower, and they were published by the authors affiliated with the institutions in China.