# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 7 Research Questions
### Specific Questions  
As for the specific questions the answers are the following. **SQ1. **: **Online vs. Offline Learning:** During the evaluation of each taxonomic class, the learning mode was a key aspect considered to determine whether explainability and interpretability techniques for drift align better through real-time adaptations (online) or batch analysis (offline). Hybrid learning modes, which combine both approaches, were also examined. The results show 24 papers for offline, 35 for online, and 23 for hybrid approaches. Online learning emerges as the predominant approach for drift explainability and interpretability systems, favoring methods where predictive models and their explanations are updated in real time. This finding directly supports GQ2, as these mechanisms are heavily employed in safety-critical systems where drift, being an inherently temporal phenomenon, requires immediate detection to minimize potential damage. In these domains, waiting for model retraining is often not feasible. Consequently, XAI itself has been pushed toward real-time capabilities. While offline batch methods typically produce richer explanations, their slower response to drift prevents them from emerging as the leading learning mode in this review. Hybrid learning modes present an interesting approach, typically pairing lightweight streaming drift detectors with checkpoints: when a detector signals a change or confidence degrades, a cached data slice is processed by an offline explainer or a more costly retraining routine. **SQ2. **: **Drift Characteristics:** The types of drift addressed within explainability frameworks were analyzed across taxonomic classes. The results show the following: general concept drift (50 papers), sudden concept drift (8), gradual concept drift (14), incremental concept drift (6), recurrent concept drift (3), and data drift (18). The sum exceeds the total number of studied papers because several papers address multiple drift types. Concept drift clearly predominates, with 50 papers utilizing concept drift in its generic form, overlooking its temporal morphologies. By remaining generic, authors develop explainability and interpretability schemas that operate at the model level rather than being process-aware, focusing more on the algorithmic layer than the temporal layer. When temporality is specified, two types prevail: gradual and sudden. Gradual drift dominates in four taxonomic classes: Distance/Divergence Geometry, Bayesian Modeling, Fuzzy Transparency, and Prototype Tracking. These classes treat concept drift as a smoothly deforming structure, whether a distance curve, belief curve, fuzzy rule weight, or moving exemplar, and this continuity produces explanations for gradual change. Sudden drift is addressed in Latent-Representation Geometry and Similarity, Human-in-the-Loop and Pedagogical XAI, Feature Attribution, and Game Theory classes. All three families pivot on contrastive reasoning, transforming sudden drift into instantly legible before/after pictures that isolate culprit features (game-theoretic attributions), visualize ruptures geometrically (latent-space jumps), or provide explanations to humans for rapid corrective teaching (pedagogical XAI). Incremental and recurrent drift types represent minimal focus, with some papers appearing in multiple categories. This likely occurs because these morphologies require long-term memory, fine-grained deltas, and nuanced user stories, characteristics that today's rapid XAI toolkits and benchmark traditions are not designed for. Data drift, though less predominant, is addressed across almost all taxonomic classes, except C2, C4, C9, and C14. This aligns with the fact that these four missing classes rely on label-aware semantics or human corrective action; pure data drift monitoring lacks this semantic anchor, consistent with the taxonomic structure. * **Q3.