# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Feature Attribution and Game Theory
#### 6.6.1 Technical and Methodological Insights  
The unifying strength of this class lies in its foundational reimagining of feature attribution techniques--SHAP, LIME, permutation importance, and their derivatives--not as static diagnostic tools, but as dynamic, embedded narratives that continuously articulate why and how a model's decision logic evolves in response to drift. Across all 19 papers, attribution shifts--whether manifested in changing importance ranks, fluctuating magnitude profiles, or spatial realignments in attention maps--are elevated into primary, human-interpretable accounts of concept evolution. This approach intrinsically links the justification of individual predictions to the longitudinal story of model adaptation, rendering separate drift detection modules largely redundant. For instance, the TSUNAMI framework developed by [PERSON] et al. aggregates nightly SHAP vectors into rolling global-importance curves and heatmaps, directly correlating the ascent of older frequency features (F1, F3) with a strategic shift in retail churn logic from spend-based to cadence-based signals [133]. Similarly, [PERSON] et al. [105] normalize and temporally stack SHAP vectors to generate clinical dashboards, where climbing curves for \"respiration rate\" or \"ambulance arrival\" visually narrate the emergence of COVID-19 respiratory distress patterns weeks before performance metrics degrade. In both cases, the same attribution values that locally explain a prediction simultaneously compose a global timeline of conceptual change, turning explanation into an early-warning system. Methodologically, these papers innovate by ensuring the attribution itself becomes a lightweight, streaming-compatible signal of drift interpretability. [PERSON] and [PERSON] [61] distill Shapley values into ultra-light surrogate regressors, enabling resource-constrained edge devices to track attribution fidelity as a real-time barometer of concept integrity. Their finding--that deviations between surrogate and reference SHAP scores spike precisely during known distribution shifts--demonstrates that explanation accuracy is a sensitive proxy for drift. The ISAGE method by [PERSON] et al. [50] and the IPFI method by [PERSON] et al. [108] further optimize this real-time capability through incremental, constant-time updates. ISAGE's exponential smoothing of Shapley-style loss differences, for example, preserves the Shapley conservation law while generating immediate vector snapshots. When applied to electricity demand streams, crossing curves for vicprice (rising) and nswprice (falling) visually articulate a shift from imputed to genuine pricing signals the moment real data arrives--without invoking external detectors. Crucially, many frameworks offer tunable lenses for causal versus correlational narratives. ISAGE's observational mode, which respects feature correlations, might mask \"commission\" importance if predictable from \"salary,\" while its interventional mode--breaking correlations--highlights commission's role when marginal distributions change. This duality provides domain experts (e.g., auditors) with tailored explanatory perspectives on root causes. The papers also showcase how attribution narratives are refined for domain-specific intelligibility. [PERSON] et al. [127] express LIME outputs as plain-language inequality rules (\"login-country \(\
eq\) KYC-country\"), allowing fraud analysts to instantly see which behavioral cues--like geolocation mismatches during COVID-19--dominate the model's evolving \"fraud\" definition. [PERSON] and [PERSON] [36] derive compact \"relevance signatures\" from permutation vectors, where a negative sum over specific features (F2, F4, F6) explicitly flags mislabeled walking/biking instances, directly guiding data correction. [PERSON] et al.