# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 2 Background: Drift Phenomena and Explainable/Interpretable AI
### Putting The Together--Drift Explainability  
While the individual concepts of 'drift' and 'explainability' are well-established in the machine learning literature, their intersection--specifically, the explainability of drift--lacks a formal, consensus definition. Current research often treats explainability as an auxiliary component of drift detection systems rather than a structured epistemological framework. This gap persists despite a proliferation of methodologies addressing interpretable drift adaptation, as cataloged in our taxonomy (Section 6). Through systematic analysis of these approaches, we observe that the explainability of drift is not merely the application of post hoc XAI techniques to drifted models, nor is it reducible to drift detection metrics. Instead, it constitutes a distinct capability: the dynamic translation of distributional shifts into human-actionable narratives intrinsically generated by the adaptation process itself. We therefore propose the following synthesizing definition, grounded in patterns observed across methodological classes: \"Explainability of drift constitutes the integrated capability of adaptive systems to transform detected changes--whether in data distributions (data drift) or input-output mappings (concept drift)--into human-interpretable narratives that intrinsically articulate why, where, and how the drift manifests, while projecting its consequences. Unlike post hoc interpretation of static models, this process embeds rationale generation directly within drift adaptation mechanics, leveraging evolving artifacts--geometric displacements in latent representations, mutations in interpretable rule structures, or domain-anchored semantic tags--to translate statistical nonstationarity into auditable, actionable insight.\" For example,  
* When sensor networks detect hydraulic anomalies, explainability moves beyond divergence metrics to reveal--through migrating prototype clusters or vanishing fuzzy clauses--that valve corrosion in Pump P402 triggered the shift;
* When fraud models lose accuracy, feature attribution timelines expose geolocation mismatches eclipsing transaction amounts as the evolving rationale.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Age** & **Sex** & **Job** & **Housing** & **Savings** & **Amount** & **Dur.** & **Purpose** \\ \hline
58 & f & unskilled & free & little & 6143 & 48 & car \\ \hline \end{tabular}
\end{table}
Table 1: CI: Feature values of a specific customer.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Age** & **Sex** & **Job** & **Amount** & **Dur.** & \(\mathbf{o_{2}}\) & \(\mathbf{o_{3}}\) & \(\mathbf{o_{4}}\) & \(\mathbf{f(x^{\prime})}\) \\ \hline  & & skilled & \(-20\) & \(0.108\) & \(2\) & \(0.036\) & \(0.501\) \\  & & skilled & \(-24\) & \(0.114\  
Crucially, these explanations are not ancillary outputs but the substance of adaptation itself: counterfactual recommendations harden against invalidating future drifts; compliance tags chronicle regulatory assimilation; and SHAP vectors crystallize into early-warning dashboards. By tethering abstract change to tangible mechanisms--corroded hardware, emergent cyber-tokens, or typhoon-altered ocean stratification--explainability bridges detection to intervention, enabling stakeholders to interrogate not merely when a model fails, but why its logic reconfigured and how to reconcile it with reality. This seamless fusion of quantification and narrative, grounded in domain semantics, fulfills the dual mandate of XAI in nonstationary environments: making drift intelligible and actionable. Thus, our definition positions explainability not as an add-on to detection but as the mechanism through which drift becomes legible, auditable, and actionable--a paradigm shift evident in techniques ranging from evolving fuzzy rule structures to geometrically anchored attribution timelines.