# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 3 Research Methodology
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criterion** & **Included** & **Excluded** \\ \hline Language & English & Other languages, such as German, Chinese, and Spanish. \\ \hline Publication type & Peer-reviewed journal articles & Book chapters, conference papers, magazine articles, reports, theses, and other gray literature. \\ \hline Recentness & Recent papers published in 2021 or after & Papers published before 2021. \\ \hline Study content & Application of XAI methods & Papers that generally described XAI or reviewed other works without describing any XAI applications. \\ \hline Quality & Papers of sufficient quality & Papers that were exceptionally short (less than six pages) or those that did not fulfill the basic requirements for a publication channel (e.g., be peer-reviewed, have an international board [35]). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Inclusion and exclusion criteria for the review of recent applications of XAI. in Table 1, along with the XAI application and the reason(s) why the authors say that explainability is essential in their domain. Data collection process, data items, study risk of bias assessment, effect measures, synthesis methods, and reporting bias assessment: To categorize and summarize the included articles in this review, the first author developed a Google Survey that was filled out for each selected article. The survey included both categorical (multiple-choice) and open-ended questions designed to systematically categorize the key aspects of the research. This approach ensured a consistent and comprehensive analysis across all articles. The survey provided an Excel file with all responses, simplifying the analysis process. Each reviewer assessed their allocated articles using the predefined codes and survey questions created by the first author. In cases of uncertainty regarding the classification of an article, reviewers noted the ambiguity, and these articles, along with their tentative classifications, were discussed collectively among both authors to reach a consensus. This discussion was conducted in an unbiased manner to ensure accurate classifications. While no automated tools were used for the review process, Python libraries were employed for quantitative assessment. Some of the developed codes (survey questions) were as follows:  
* What was the main application domain, and what was the specific application? * In what form (such as rules, feature importance, counterfactual) was the explanation created? * Did the authors use intrinsically explainable models or post-hoc explainability, and did they focus on global or local explanations? * How was the quality of the explanation(s) evaluated? * What did the authors say about why the explainability of their specific application is important? (Open-ended question.) Figure 2: PRISMA flow chart of the study selection process. After completing the coding process and filling out the survey for each included article, we synthesized the data using both qualitative and quantitative techniques to address our research questions [39]. Qualitatively, we summarized the characteristics of the included studies based on the predefined codes. Quantitatively, we performed statistical analysis of the data, utilizing Python 3.11.5 to extract statistics from the annotated Excel table. This combination of qualitative and quantitative approaches, along with collaborative efforts, ensured the reliability and accuracy of our review process. To assess the risk of reporting bias, we examined the completeness and transparency of the data reported in each article, focusing on the availability of results related to our predefined research questions. Articles that lacked essential data or failed to report key outcomes were flagged for potential bias, and this was considered during the certainty assessment.