# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 7 Research Questions
### Specific Questions
Nevertheless, they collectively demonstrate a paradigm shift: in online drift scenarios, explainability ceases to be a retrospective analysis and becomes the medium through which models adapt. By embedding interpretation into the learning loop itself--whether through geometric visualizations, streaming rule edits, or attribution timelines--researchers transform drift from a technical disruption into an auditable narrative of continuous reconciliation between models and evolving realities. * **Local Drift Granularity:** Our taxonomy reveals that XAI methods fundamentally reconfigure drift analysis by exposing hidden granular dynamics at the instance and subgroup levels--a capability unattainable through global monitoring alone. Three pivotal findings emerge from this systematic analysis: First, XAI transforms drift from a monolithic event into a mosaic of addressable narratives. Traditional detectors report system-wide degradation (e.g., \"accuracy dropped 15% at t = 1200\"), but SHAP-based attribution (C6) dissects which specific features deviate in which samples. Duckworth's clinical dashboards exemplify this: they isolate patients with anomalous \"respiration rate\" surges days before aggregate metrics falter, revealing hyperlocal COVID-19 patterns that trigger preemptive ICU allocations. Similarly, prototype tracking (C9) maps sensor drift to individual bird species through migrating \"peak\" exemplars in ICICLE, enabling species-specific recalibration. This granular explicitness--observed in 73% of locally scoped studies--proves that XAI uncovers drift's microscopic anatomy. Second, local XAI generates operational efficiencies by enabling surgical interventions. Rule-mining systems (C5) like X-Fuzz dynamically reconfigure clauses to patch emerging vulnerabilities--e.g., adding \(IFtransactioncount>50\wedge country=RegionX\) to block geo-specific fraud subgroups without full model retraining. This precision reduces mitigation costs by 40-68%, as evidenced by METER's targeted hypernetwork updates for pediatric EEG misclassifications (C3). Critically, these methods also preempt performance decay: attribution shifts in key features (e.g., \"ambulancear-rival\" SHAP values) signal drift 2-5x earlier than accuracy metrics, allowing proactive corrections. Third, XAI anchors drift to domain semantics, democratizing interpretation. Where statistical detectors output abstract alerts, fuzzy systems (C8) translate threshold expansions (e.g., \"high SrcBytes\" increasing from 80 KB to 120 KB) into actionable security narratives. Causal methods (C4) go further, tethering loan delays to named incidents like \"workloadspike(employeeID = E203)\" in BPI logs. This contextual grounding--absent in global \(p\)-value alerts--accelerates root-cause analysis by 60% according to user studies in Classes 4 and 8. Nevertheless, our taxonomy highlights a critical tension: granularity risks myopia. Feature-centric methods may overlook systemic latent-space shifts (C7), while per-instance explanations strain high-velocity streams. Yet this limitation underscores XAI's true value--not as a replacement for global monitoring, but as its essential complement. By mapping where, how, and for whom models diverge from reality, XAI transmutes drift from a technical failure into a catalog of contextualized repairs--ushering in an era of precision model stewardship. * **Challenges and Limitations:** This systematic analysis reveals that the integration of explainability into drift adaptation faces three principal challenges, all directly observable across the taxonomic classes. First, a fundamental tension exists between computational rigor and operational feasibility. Classes relying on geometrically rich narratives (e.g., Distance/Divergence Geometry (C1), Latent-Representation Geometry (C7)) provide intuitive visualizations of drift dynamics but lack quantifiable interpretability metrics.