# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy  
This section surveys the explainability and interpretability techniques applied to models operating under data or concept drift. To organize the discussion, we classify the primary studies by the technical and methodological foundations underlying each approach. An application-domain taxonomy was considered initially but abandoned after a close reading of the corpus revealed that most papers are intentionally domain-agnostic or rely on synthetic benchmarks; grouping by domain would therefore have produced arbitrary and sparsely populated categories. A technique-oriented taxonomy better suits the scientific focus of this review and enables a clearer, method-oriented comparison. Techniques are further organized into five overarching macro-classes. Statistical and Probabilistic Reasoning includes methods based on statistical measures, divergences, and probabilistic frameworks to identify and explain drifts. Feature Importance and Attribution covers approaches that attribute significance to specific features, examples, or structural elements, explicitly showing how changes in these components contribute to drift. Interactive and Human-Centric Approaches encompasses techniques emphasizing human collaboration and practical decision-making contexts for drift interpretation. Logic and Rule-Based Methods comprises methods leveraging logical, causal, and fuzzy rules to clarify the nature of detected drifts. Finally, Evaluation and Auditing focuses on methods for assessing changes in model performance, fairness, and compliance, underscoring the necessity of transparent and justifiable drift explanations. These macro-classes group methods based on shared conceptual and methodological themes, ranging from statistical measurement of drift phenomena to human-centered interpretative frameworks. A conceptual representation of these macro-classes, highlighting their hierarchical and methodological connections, is illustrated in Figure 18. The representation is realized as a phylogenetic tree, created using a Miro board. While the macro-classes provide conceptual organization, the detailed methodological analysis and comparative evaluation presented in this review are specifically conducted at the level of fifteen micro-classes. Each subsection begins with a detailed analysis of the techniques used for explaining and interpreting the drift within the papers of each class. This is complemented by a tabulated summary of public datasets used in the cited studies. The section culminates in a multidimensional class evaluation that probes the collective utility and limitations of the techniques through six interconnected perspectives. First, the analysis interrogates the type of drift addressed, distinguishing between data  
Figure 17: The network of collaboration between researchers, created using VOSviewer software. (**a**) Visualization of the complete collaboration network. (**b**) A zoom-in on two different clusters. drift and concept drift (and its subtypes). Building on this, it examines application contexts where domain-specific imperatives shape methodological priorities. The scope of each approach is then scrutinized: does it pinpoint drift at the granular, instance-level (local) scale, or diagnose systemic shifts across the entire model (global)? This distinction informs the intended audience, as explanations must balance technical precision for data scientists with intuitive accessibility for end-users or domain experts. To ground these considerations, the analysis evaluates quantitative metrics that empirically validate claims. Finally, it assesses learning modes, probing whether methods align with real-time adaptation (online), retrospective batch analysis (offline), or hybrid frameworks that straddle both paradigms. Where a subsection contains papers sharing common datasets, direct comparisons of results and methodological trade-offs are highlighted.