# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Graph Attention and Structural Reasoning
#### 6.10.1 Technical and Methodological Insights  
The Graph Attention and Structural Reasoning class pioneers a paradigm where explainability is not merely appended to drift detection but structurally embedded within the interpretation itself. At its core, this approach leverages spatial and relational representations--be it household movement graphs, video grids, or IoT interaction networks--to transform abstract distributional shifts into tangible, human-actionable narratives. [PERSON] et al. [97] establish this foundation by treating daily sensor data as self-explanatory graphs, where attention weights dynamically spotlight behaviorally significant locations like bathroom-bedroom transitions before clinical alerts manifest. Crucially, their system personalizes drift thresholds to individual baselines, delivering alerts as ranked room deviations rather than statistical anomalies--a design validated when field nurses prioritized these spatial lists over raw divergence metrics. This spatial grounding is further amplified by the GridHTML framework introduced by [PERSON] et al., which replaces numerical anomaly scores with an intuitive visual language: video frames are tessellated into cells that \"redden\" precisely where motion or appearance deviates from learned norms [131]. Like [PERSON]'s attention maps, this heatmap localizes drift spatially--flashing red in a traffic lane while parking bays stay green--but achieves even greater immediacy by eliminating interpretation barriers entirely. Operators naturally decoded drift through accumulating redness in unstable patches, confirming that pixel-level attribution transforms drift from a metric into a visceral, visual story. Building on this spatial explicitness, [PERSON] et al. [46] confront the temporal vulnerability of explanations themselves. Their RoCourseNet framework anticipates how drift might invalidate counterfactual recommendations (\"recourses\") by simulating worst-case data shifts during training. Here, the adversarial retraining loop engineers recourses to remain valid under both current and hypothetical future models, quantifying their resilience through a robust-validity score. This metric does not just detect drift; it certifies the explanation's shelf life--turning recourse feasibility into a human-readable guarantee that drift has been neutralized. When validity scores diverge, analysts instantly see drift outpacing adaptability; when robust validity holds, users trust that \"how-to-improve\" actions will endure. This temporal safeguarding represents a conceptual leap beyond spatial localization, treating explanation robustness as integral to interpretability. Finally, [PERSON] et al. [38] synthesize spatial and causal reasoning for federated IoT environments. Modeling smart-home rules as interaction graphs, their system identifies drift through SHAP-based sub-graphs that expose malfunctioning automation chains--like \"smoke \(\rightarrow\) water-valve \(\rightarrow\) kitchen leak\"--with spiked edge weights. By highlighting which trigger-action pathway triggered retraining, FexIoT answers the \"why\" missing in purely spatial approaches. Analysts consistently ignored statistical alarms to act solely on these sub-graphs, proving that causal attribution--not detection thresholds--delivers actionable insights. This evolution--from [PERSON]'s \"where\" (deviating rooms) to [PERSON]'s \"why\" (faulty automation chains)--demonstrates how structural reasoning matures across the class. Collectively, these papers share a rejection of opaque metrics in favor of intrinsically interpretable artifacts: attention-ranked rooms, self-annotating heatmaps, drift-proof recourses, and causal sub-graphs. Whether through visual immediacy, adversarial hardening, or causal transparency, they ensure that every drift alert arrives with its explanation structurally interwoven--not as an afterthought but as the very fabric of detection. As such, symbolic approaches exemplify white-box modeling and offer direct interpretability by design, labeling this class as Interpretable AI.