# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Change-Point and Statistical-Test Theory
#### 6.2.1 Technical and Methodological Insights  
The \"Change-Point and Statistical-Test Theory\" class fundamentally reimagines drift detection as an exercise in narrative construction, where statistical rigor serves not merely to signal change but to articulate its context, cause, and consequences in domain-grounded terms. This paradigm emerges from a shared conviction: the mechanism of detection must inherently generate its own explanation. [PERSON] et al. 's [PERSON] et al. (2019) Driftage framework pioneers this ethos by transforming the ADWIN algorithm's outputs into an auditable dialogue among specialized agents. Each decision--from the Monitor's data ingestion to the Executor's alarm dispatch--is stamped with forensic evidence: sensor IDs, adaptive sensitivity settings (like \(\delta\)), and timestamps. Crucially, the Planner's requirement for multi-sensor agreement does not just reduce false positives; it explains credibility through corroboration (\"two muscles concurred\"). This provenance culminates in visual narratives like parallel-coordinate plots, where a spike across specific channels (e.g., hamstring and thigh) instantly maps where behavior diverged. Even \(\delta\) adjustments become part of the story--a shift from 1.0 to 0.1 transparently narrates rising noise levels. Thus, Driftage establishes the class's core tenet: statistical parameters are not opaque thresholds but characters in a diagnostic drama. Building on this, [PERSON] and [PERSON]'s ExStream [104] reframes the actual signal of drift as a shift in explanatory semantics. By replacing accuracy metrics with Shapley-style feature contributions, it detects drift when the geometry of influence reconfigures--say, \"ODOR = almond\" collapses while \"GILL-SIZE = crowded\" surges. This approach inherently answers why a model fails: the attributes steering predictions have realigned. Visualized as evolving line plots of feature contributions, the framework lets operators trace both macro-level drift patterns and micro-level attribute stories simultaneously, rendering abstract alerts obsolete. [PERSON] et al. 's CDLEEDS [48] extends this logic locally, arguing that drift is most interpretable at the cluster level. When SHAP attributions for a subgroup cease summing to predictions--flagged by a t-test--the system declares: \"the story of feature importance here has broken.\" Flipped SHAP arrows (e.g., from \((x1)\) to \((x2)\)) visually anchor this breakdown, proving that localization and explanation are inseparable. Together, these frameworks demonstrate that explanatory vectors are not just inputs to detectors--they are the detectors, transforming statistical tests into translators of model intent. This evolution toward spatially and contextually enriched narratives continues with [PERSON] et al. 's BNDM [123], which replaces sliding-window p-values with a cartography of change. By decomposing the data space into a Polya Tree of semantic bins (e.g., \"high-speed, low-torque quadrant\"), drift manifests as color-flipping histograms where instability is mapped to coordinates. A bin lighting up at coarse resolution (\"left of 0.3\") invites zooming to pinpoint the precise band in crisis, while partition names like \"voltage range 220-240 V\" pre-translate alerts into domain language. [PERSON] et al. [116] then shift the focus to temporal analogy, treating drift as a call to retrieve historical precedents. Residual anomalies trigger searches in an embedding database, returning matches like \"Building B's shutdown (May 2022)\" or \"HVAC failure (July 2021).\" The resulting narrative--\"today's pattern resembles last spring's LED retrofit\"--embeds the why within retrievable context, with metadata-enriched warehouses (\"FactDrift tables\") cementing links to events like \"forecast error +18% during 38 \"C heat.\" Here, the statistical residual becomes a query for narrative restitution. Finally, [PERSON] et al. [40] weave causality into the fabric of change-point theory, framing drift as a dialogue between log perspectives.