# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Distance/Divergence Geometry
#### 6.1.1 Technical and Methodological Insights  
The Distance/Divergence Geometry class collectively reimagines the concept drift explanation by transforming abstract statistical shifts into intuitive geometric narratives, where distances and divergences in latent or feature space become self-contained stories of change. Rather than treating detection and interpretation as separate processes, these methods embed explainability directly into their core artifacts, allowing analysts to \"read\" drift through evolving visual or quantitative geometry. At the heart of this approach lies a unifying principle: the same geometric construct that quantifies drift simultaneously explains it. For instance, [PERSON] et al. 's with their STAD framework, pairs latent-space distributions with auto-encoders, framing drift as a state-transition graph where symmetrized KL-divergence not only triggers jumps between states but also justifies them--reusing a past state, visualized through overlapping KDEs, instantly communicates seasonal recurrence, while spawning a new state signals novel behavior [130]. This collapses detection, adaptation,  
Figure 18: Taxonomy “phylogenetic” tree. and rationale into a single, auditable timeline. Similarly, [PERSON] et al. 's MCD-DD [37] converts Euclidean distances between concept vectors into heatmaps where the pattern of gaps--sudden flares, twin triangles, or fading diagonals--becomes a visual language for drift style. Practitioners recognize these shapes at a glance, trusting their geometric immediacy over traditional significance tests. The class further deepens interpretability by anchoring explanations in tangible, inspectable artifacts. [PERSON] et al. 's [140] DSE leverages the evolving geometry of SVM decision boundaries: widening margins, miscolored points spilling across frontiers, and jumping density counters narrate drift in real time, while side-by-side \"before/after\" plots of boundary realignment show why a new classifier restores separability. [PERSON] et al. [121] extend this to time series via Empirical Mode Decomposition, where fading amplitudes of interpretable IMFs (e.g., a weakening 24-hour sinusoid) directly link drift to behavioral shifts like flattened night-time consumption. Analysts trace forecast revisions to specific IMFs, turning feature selection logs into diaries of changing demand mechanics. [PERSON] and [PERSON] [63] operationalize this by plotting multivariate hydraulic data as distance vectors against a median trace--spikes in specific channels (e.g., \"Temperature 1\") immediately point to valve wear, transforming high-dimensional streams into fault maps. Here, the shape of the deviation is the explanation, requiring no auxiliary statistics. Later papers enhance this foundation by layering attribution on top of divergence metrics, explicitly linking global drift to localized feature-level changes. [PERSON] et al. 's [122] CDDIA exemplifies this: KL-divergence between reconstruction-error histograms first locates drift via optimization masks that bucket samples into \"outdated,\" \"valid,\" or \"shifted\" categories; SHAP analysis then attributes the shift to specific components (e.g., pump P402), turning feature weights into a ranked anatomy of root causes. [PERSON] et al. 's TRUST-LAPSE [96] similarly fuses Mahalanobis distance and cosine similarity into \"mistrust scores\" tied to inspectable exemplars--a surge in scores reveals why a sample deviates (e.g., pediatric EEGs entering an adult-trained model) by referencing nearest-neighbor prototypes from a coreset. This evolution--from detecting divergence to explaining its origin--reflects the class's methodological maturation. Crucially, several frameworks treat dynamic geometry itself as the explanation. [PERSON] and [PERSON]'s [62] adaptive NGPCA reifies drift through jumps in local subspace dimensionality (\(m\)): a cluster expanding from 10 to 13 components signals emergent operational complexity, with the eigenvalue recalibration loop visually justifying the model's adaptation.