# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Performance Baseline and Metric Audit
#### 6.12.1 Technical and Methodological Insights  
This class pioneers a paradigm where the very metrics tracking model performance are designed to be the explanation of drift, transforming cold diagnostics into a continuous, human-interpretable narrative of change. [PERSON] et al. [105] lay the foundation by demonstrating how the temporal dynamics of normalized SHAP values--aggregated weekly into a public dashboard--directly reveal why predictions evolve. When features like respiration rate, triage discriminator, and ambulance arrival spiked simultaneously in March 2020, clinicians immediately understood that the model's clinical focus had shifted toward respiratory distress, long before AUROC significantly degraded. Crucially, this SHAP timeline does not just detect anomalies; it explains their nature by distinguishing data drift (e.g., surging admission rates) from concept drift (e.g., vital signs eclipsing chief complaints) through the interplay of feature contributions and performance trends, preemptively signaling retraining needs. Building upon this principle of feature-centric explanation, [PERSON] et al. [29] extend the temporal lens to quantify the stability of the model's reasoning itself. By tracking Spearman correlations in bootstrapped, clustered feature rankings across sliding windows, they expose insidious shifts in the prediction rationale--such as 40% of log-message features reversing their impact direction--even when the AUC remains stable. This correlation metric directly explains why past interpretations become obsolete and operationalizes \"explanation freshness\" by treating window length as a hyperparameter to balance recency against reliability, thereby framing drift as an erosion of trust in the model's logic. Complementing this feature-level focus, [PERSON] et al. [57] reframe the drift explanation through the evolving relationship between model accuracy and complexity. Their dashboard plots the monthly AUC against the mean number of active predictors in inherently interpretable models (e.g., LASSO regression, rule sets). The countermovement of these metrics--AUC softening as predictor counts plummet when historical data re-enters the sliding window--tells the story of drift as a deteriorating accuracy-complexity trade-off. Clinicians intuitively grasp that rising complexity may temporarily sustain performance until outdated patterns conflict with current realities; the tipping point where this balance collapses is the onset of concept drift, explained without auxiliary statistics. This dual-curve approach contextualizes how covariates lose relevance, how feature filtering preserves usability, and when retraining becomes imperative. This progression culminates in [PERSON] et al. 's paper [115], who anchors the drift explanation to a universally comprehensible baseline: the error of a constant median predictor. Their normalized MAE (nMAE)--the ratio of the model's MAE to this baseline's MAE--transforms a scalar metric into an immediate interpretation of drift impact. Values near 1 declare the model useless (\"guessing the median\"); values above 1 indicate harmful drift; values below 1 confirm residual utility. Paired with a scatter plot of training-vs.-test MAE, deviations from the diagonal visually classify drift as benevolent or hostile, explaining the degradation severity and type (e.g., the sepsis log's pervasive feature futility vs. the helpdesk log's robustness) far earlier than accuracy thresholds. Together, these papers form a coherent intellectual stream: they reject post hoc interpretability by embedding explanation into the audit process itself. [PERSON] and [PERSON] expose the internal logic shifts (through SHAP and ranking instability), while [PERSON] and [PERSON] elucidate the external consequences (via complexity trade-offs and baseline comparisons). All converge on using temporal metric evolution--whether SHAP averages, ranking correlations, AUC predictor curves, or nMAE trends--as a visual, intuitive language to narrate drift.