# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Latent-Representation Geometry and Similarity
#### 6.7.1 Technical and Methodological Insights  
The papers in this class collectively demonstrate that the evolution of geometric relationships within learned latent spaces provides an intrinsic, actionable explanation for concept drift, transforming abstract statistical shifts into human-interpretable narratives. [PERSON] and [PERSON] (2019) initiate this paradigm by treating classifier embeddings as a \"live map\" of data reality: their outlier scores, visualized through stark histogram contrasts, immediately implicate specific regions of latent space where novel defects emerge, allowing engineers to inspect high-score images and see the unfamiliar visual patterns (e.g., edge roughness) that triggered the drift alarm. [PERSON] et al. deepen this spatial logic by exposing drift through changes in the fuzzy rule hierarchies governing multi-stream latent alignment. When a rule's dominance slips (e.g., \"IF Temp = 35 \"C THEN Alert\" fades), the method not only flags drift but explicitly identifies which feature range lost discriminative power, rendering the shift a semantic edit to the rule itself--no error curves required. [PERSON] and [PERSON] (2018) extend this to behavioral contexts with their \"living scrapbook\" memory network: drift manifests as a visible migration of attention weights toward rarely used memory slots, highlighting anomalous attributes (e.g., \"Location = Tokyo, Amount > \(\copy\)5000\") that form an instance-level rationale for the shift. Crucially, all three methods anchor explanations in domain-meaningful semantics--visual traits, rule thresholds, transaction attributes--avoiding opaque distance metrics. [PERSON] et al. (2018) and [PERSON] et al. (2019) further prove that drift explanations can reside within model internals. [PERSON] exposes degenerating neuron \"distinguishability\" in malware detection: collapsing bimodal activation distributions (e.g., neuron 23's eroded bimodality) visually signal which internal decision boundaries have blurred, directly linking performance decay to specific degraded components. [PERSON] synchronizes generative drift detection with pixel-level relevance heatmaps: practitioners simultaneously see a quality curve dip and the shifting visual traits (e.g., lace \(\rightarrow\) cuffs) that the discriminator now deems decisive, converting latent decay into an intuitive \"before-and-after\" story of feature relevance. [PERSON] et al. then showcase how engineering latent geometry for domain interpretability elevates explanations: their wavelet-derived \"ridge\" coefficients embed geological semantics into the representation, so drift is explained through a ranked list of altered stratigraphic cues (e.g., ridges marking depositional cycles), enabling geologists to relabel only depths where new cycles emerge. This contrasts with the dual geometric narrative proposed by [PERSON] et al. for unlabeled streams: they expose drift through trajectories of concept vectors (e.g., increasing centroid velocity for \"running\") and shifts in actor participation weights (e.g., \"60% walking \(\rightarrow\) 40% running\"), rendering behavioral changes as intuitive fractions on a probability simplex [39]. The fil rouge is geometric transparency as an explanation. Whether through histograms pinpointing anomaly clusters, rule edits specifying degraded thresholds, attention maps highlighting anomalous attributes, neuron diagnostics localizing eroded boundaries, or ridge lists flagging geological shifts, all methods leverage the distortion of latent structures to answer \"why\" drift matters. The differences lie in granularity--instance-level ([PERSON]), feature-level ([PERSON]), or model-internal ([PERSON])--but all share a core thesis: latent space is not merely a detection arena but an explanatory canvas. By visualizing how embeddings, rules, neurons, or concepts move or lose separation, they convert drift from a statistical alarm into a legible story of change, directly guiding interventions like targeted relabeling or rule updates.