# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Human-in-the-Loop and Pedagogical XAI
#### 6.14.1 Technical and Methodological Insights  
The papers in this class fundamentally reposition explainable AI (XAI) artifacts as the primary medium for interpreting and responding to concept drift, rather than treating explanations as mere supplements to separate detection mechanisms. Common to all approaches is the integration of XAI directly into the learning loop, where explanations serve a dual purpose: (1) they reveal drift by exposing model reasoning flaws or shifts, and (2) they mediate human-AI collaboration to correct drift. [PERSON] et al. [141] leverage local LIME explanations (token highlights + probabilities) presented during active learning queries. These explanations act as a \"pedagogical channel\": they immediately show annotators why the model struggles (e.g., highlighting outdated tokens during drift), enabling targeted label corrections. Crucially, the study demonstrates that these explanations train human annotators, leading to lasting efficiency gains even after explanations are removed--showing that interpretability itself mitigates future drift impact by upskilling humans. [PERSON] et al. [45] deepen this by using dual post hoc explanations (LIME for local feature attribution + SHAP for global feature ranking) specifically triggered by false detections indicative of drift. Here, explanations directly quantify drift severity: the magnitude of performance improvement (e.g., precision jump from 0.70 to 0.75) after human analysts apply domain-weight corrections (based on the explanations) becomes an interpretable metric of how far the model's reasoning had diverged from reality. The continuous \"teacher-student\" feedback loop ensures that new counter-evidence (false positives/negatives) automatically surfaces as fresh explanations, making drift interpretation an ongoing, integral process. [PERSON] et al. [143] shift the focus to global interpretability with their incremental PDP (iPDP). The evolving partial dependence curve visually encodes both virtual and real drift in the model's logic: horizontal shifts indicate feature distribution drift, while changes in curve shape (e.g., flat \(\rightarrow\) U-shaped \(\rightarrow\) monotonic) explicitly reveal how the model's decision boundary or feature relationships have changed over time. The iPDP's geometry (slope, direction, position) serves as an intuitive, real-time explanation of drift dynamics, eliminating the need for auxiliary detection methods. The key evolutionary thread is the progression from explanations aiding drift correction ([PERSON]), to explanations enabling drift quantification and continuous adaptation ([PERSON]), and finally to explanations providing an intrinsic, visual representation of drift mechanics ([PERSON]). While [PERSON] and [PERSON] focus on local explanations triggering human intervention, [PERSON] offers a global, model-wide perspective. The unifying thread is that drift is not merely detected but interpreted and addressed through the XAI artifact itself, embedding explainability as the core mechanism for maintaining model resilience in dynamic environments. Explanations transition from diagnostic tools to active pedagogical and operational components of the drift adaptation loop. Therefore, this places this class within the XAI paradigm.