# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 7 Research Questions
### Specific Questions
Thus, the main opportunity derived from these explainability techniques is directly connected to stakeholders' accountability requirements: the higher the responsibility and external scrutiny in a domain, the greater the benefit stakeholders derive from detailed explanation. * **SQ8. ****Online Adaptation of XAI:** Traditional XAI techniques face fundamental incompatibilities with streaming environments where concept/data drift demands real-time interpretability. Our taxonomy reveals that researchers overcome inherent incompatibilities through foundational re-engineering of XAI paradigms, transforming them from post hoc diagnostic tools into embedded mechanisms that continuously narrate drift. This adaptation unfolds through four interconnected conceptual shifts observed across technical classes. One prominent strategy involves the incremental approximation of explanations, where computationally intensive techniques like Shapley value calculation or rule induction are reimagined as lightweight, iterative processes. For instance, in the Feature Attribution and Game Theory class, frameworks such as TSUNAMI avoid recomputing global SHAP values from scratch by aggregating nightly attribution vectors into rolling importance curves. This preserves interpretative fidelity while accommodating streaming data. Similarly, rule-based systems in the Rule/Logical Pattern Mining class (e.g., X-Fuzz) trigger localized rule updates only when prediction errors exceed thresholds, ensuring that linguistic explanations evolve synchronously with model adjustments without full retraining. A second adaptation centers on architectural distillation, where complex XAI methods are replaced by efficient surrogates tailored for resource-constrained environments. The Fuzzy Transparency class exemplifies this: OSSR-NNRW maintains real-time interpretability by dynamically recalibrating sparse neuro-fuzzy weights, translating coefficient shifts into human-readable drift indicators (e.g., the disappearance of \"oxygen-enrichment flow\" weights signaling an operational change). Likewise, prototype-driven methods in Prototype/Medoid and Exemplar Tracking (e.g., ICICLE) anchor new exemplars to historical clusters, minimizing computational overhead while preserving visual narrative continuity. Critically, the most innovative adaptation lies in unifying detection and explanation. Here, metrics originally designed solely for drift identification are repurposed as self-contained explanatory artifacts. The Distance/Divergence Geometry class demonstrates this elegantly: STAD reuses KL-divergence measurements--initially quantifying latent-space shifts--to generate state-transition graphs that simultaneously detect and justify drift (e.g., overlapping kernel density estimates visually communicating seasonal recurrence). Similarly, in Statistical Tests and Change-Point Theory, ExStream replaces conventional accuracy-based alarms with Shapley-derived feature influence scores, ensuring that statistical triggers inherently localize root causes (e.g., a t-test failure coinciding with collapsing feature importance for \"ODOR = almond\"). Finally, stream-optimized interpretative interfaces transform abstract drift into actionable narratives. Graph attention methods like GridHTML encode deviations as spatial heatmaps where video cells \"redden\" in real time at loci of behavioral changes, bypassing statistical literacy barriers. Meanwhile, Bayesian and Uncertainty Modeling systems (e.g., METER) translate Dirichlet distribution shifts into plain-language confidence statements (\"evidential certainty for known concepts dropped from \(\alpha\) = (30, 2) to (6, 5)\"), anchoring uncertainty quantification to operational storytelling. Theseadaptations incur trade-offs: causal methods (Class 6.4) struggle with online scalability due to Granger-testing overhead, while incremental SHAP approximations may sacrifice theoretical guarantees for speed. Moreover, linguistic systems rely on domain experts to validate evolving rules--a bottleneck in fully automated pipelines.