# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 8 Conclusions  
This paper presented a taxonomy based on the technical-methodological foundations of explainability and interpretability concerning concept and data drift. Recently, as demonstrated in the Introduction, explainable artificial intelligence (XAI) has gained significant popularity due to its capacity to transform black-box approaches into transparent ones. This trend has led to increased interest, prompting the development of new models, methodologies, and approaches addressing the explainability and interpretability of drift across various application domains, as thoroughly demonstrated in this systematic literature review (SLR). This SLR aimed to address two distinct sets of research questions: general questions that could primarily be answered through a bibliographic overview, and specific questions targeting nuanced aspects of drift, explainability, interpretability, and their interconnections. To this end, a taxonomy was devised, grouping papers into 15 distinct classes. The taxonomy not only facilitated a structured analysis, enabling comprehensive answers to the research questions, but also provided a valuable research tool for future studies in this domain. Several key insights emerged from the taxonomy and the analysis of these classes. Firstly, the majority of research focuses on the explainability of concept drift, often overlooking its temporal dimensions and different morphologies. Online learning emerged as the predominant learning mode, primarily due to its capability to update predictive models and their explanations in real time. Nevertheless, hybrid learning modes, combining real-time detection with batch analyses, represent a promising direction for future methodological innovation, providing an optimal balance between immediacy and accuracy. The review highlighted two significant gaps in the current literature. Firstly, there is a notable absence of benchmark datasets, limiting experimental reproducibility and promoting ad hoc solutions rather than standardized, comparable research outcomes. Secondly, there is a lack of standardized quantitative metrics, which are crucial for facilitating comparative analyses across different studies. Currently, metric usage remains isolated and fragmented, preventing the establishment of a common evaluative framework. Addressing these gaps would significantly advance the field by enabling consistent, reproducible, and comparable research. A further aspect identified by this taxonomy relates to application domains. The review clearly showed that external responsibility and human safety concerns, not merely the occurrence of drift, drive interest in explainable drift methodologies. Fields characterized by high reputational or legal stakes, such as healthcare, finance, energy, manufacturing, aviation, cybersecurity, and industrial process control, have already started to adopt transparent or at least interpretable approaches due to the severe consequences associated with predictive errors. Consequently, the primary target audience for research in these areas comprises domain experts and data scientists, stakeholders who require detailed explanations to justify predictions, comply with regulations, and effectively manage risk. However, given the transformative nature of explainability, from black-box to white-box approaches, there should be an intentional shift towards making explainability and interpretability accessible to all stakeholders, including end-users. Democratizing the understanding of drift explanations would ensure the broader acceptance and practical application of these methodologies across the entire value chain. By analyzing various aspects of each taxonomic class, this review identified strengths, insights, and critical gaps in the current literature. Ultimately, this taxonomy aims to serve as a comprehensive classification and analytical tool, highlighting the current state-of-the-art and providing actionable insights to guide future research efforts in the explainability and interpretability of concept and data drift.