# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 3 Systematic Literature Review Methodology and Selection Process  
This systematic literature review draws on four leading scholarly databases:  
* Scopus;
* Web of Science;
* IEEE Xplore;
* ACM Digital Library. They were selected for their broad disciplinary coverage and established reputations. To capture the fullest possible set of relevant studies while maintaining academic rigor, we used each platform's Advanced Search interface and executed the unified search query shown in Listing 1:  
```
1(explainabilityORexplainableOR\"explainableai\"ORinterpretability)
2 AND
3(\"datadrift\"OR\"conceptdrift\"OR\"datashift\")
```  
**Listing 1** Unified search query employed across databases. Because the review centers on the intersection of explainability/interpretability and drift, the first clause targets at least one of the former terms, while the second clause anchors the search in drift-related phenomena. Syntax was adapted to each database, and searches were confined to the Abstract field to keep the result set manageable; expanding to keywords, full text, or even references would have produced an unmanageably large corpus. No publication-date restrictions were applied. As the next section shows, research in this area has only begun to flourish in recent years; introducing a temporal cutoff would therefore have unduly narrowed the search and yielded too few studies. The multi-stage selection process is summarized in the SIGMA flow diagram in Figure 14 and detailed below:  
1. **Database Search:** The query was executed in all four databases, and the results were imported into Zotero. 2. **Duplicate Removal:** Duplicate records were identified and deleted. 3. **Document-Type Filter:** Reviews, books, and book chapters were excluded so that only primary research in the form of journal articles and conference papers remained. 4. **Source-Quality Filter:** To ensure a baseline of methodological quality, we discarded papers from rank-C conferences, whereas journal articles from all quartiles were retained because a lower quartile does not in itself compromise scientific validity. 5. **Title and Abstract Screening:** The surviving titles and abstracts were then examined manually, eliminating studies that were clearly out of scope. 6. **Language Filter:** Only English-language publications were included to guarantee full comprehensibility. 7. **Data Extraction:** Relevant data were extracted from the included studies and analyzed in order to better discuss the approach and present the most common datasets and models for each identified field. The initial search retrieved a total of 239 records as follows: 122 from Scopus, 68 from Web of Science, 25 from IEEE Xplore, and 24 from the ACM Digital Library. Removing 103 duplicates left 136 unique studies. Eleven of these were reviews, books, or book chapters, reducing the total to 125. Venue-based filtering then excluded 16 papers from rank-C conferences and 7 from unranked venues, yielding 102 candidate studies. During title and abstract screening, 10 papers were removed because, although they mentioned explainability or interpretability, they did not substantively connect these concepts to mechanisms for explaining data or concept drift, a fundamental inclusion requirement. Full-text retrieval for the remaining 92 studies led to the exclusion of one article written predominantly in Chinese. Each of the 91 English-language papers was subsequently read independently by all three authors. Nine were rejected at this stage because they still failed to meet the drift-explainability criterion. [PERSON] et al. [PERSON] et al. (2018) describe only a post hoc XAI wrapper and offer no drift-diagnostic technique. The article by [PERSON] et al. [PERSON] et al. (2018) is a position paper that identifies the need for explainability in self-adaptive systems but proposes neither a drift model nor an explanatory approach. [PERSON] et al. [PERSON] et al. (2018) mention interpretability only tangentially in an IoT-monitoring context, without addressing drift. [PERSON] et al. [PERSON] et al.