# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Bias/Fairness Through Time
#### 6.13.1 Technical and Methodological Insights  
The \"Bias/Fairness Through Time\" class pioneers a paradigm where explainability artifacts transcend static interpretation to become dynamic diagnostic tools, continuously narrating how fairness erodes or adapts under drift. This approach crystallizes in complementary methodologies that progressively deepen our interpretive lens. At its foundation, [PERSON] et al. [42] establish that meaningful drift interpretation requires contrasting model behaviors across time. Their dual-framework transforms conventional explainability outputs into temporal sensors: the \(\Delta\)Shapley timeline directly harnesses the annual recomputation of Shapley values--an inherent byproduct of retraining--to quantify changing reliance on sensitive attributes. When contributions to predictions for protected groups (e.g., citizenship) intensify while group fairness metrics remain stable, this trajectory instantly reveals a critical trade-off: the model sacrifices individual equity to maintain demographic parity. Simultaneously, their FairX framework extends this contrastive logic to the rule-based structure of fairness-constrained models. By converting successive models into comparable surrogates (binary decision diagrams) and highlighting emergent, vanished, or modified paths involving sensitive features, FairX exposes how logical conditions for equitable outcomes morph over time. For instance, a new rule demanding \"non-citizen \(\wedge\) income \(>\) 630k\" signals an interpretable \"fairness regression,\" directly linking drift to discriminatory logic evolution. Crucially, these techniques intertwine: the \(\Delta\)Shapley magnitude flags when bias worsens, while FairX rules explain how it manifests in decision pathways, together forming a cohesive story of fairness drift. Building upon this foundation of model-internal contrast, [PERSON] and [PERSON] [145] shift the interpretive focus to the operational interaction stream between users and models, demonstrating how explainability itself can be weaponized as a real-time drift audit. They ingeniously repurpose counterfactual explanations (CFEs)--typically, static suggestions for recourse--into a living diagnostic system. Each user interaction with a CFE generates a hypothetical data point, but before these points seed retraining, a fairness-conscious screening interprets potential drift: ADWIN windows detect statistical shifts while balanced ensembles predict new labels, rejecting points that risk amplifying bias. This transforms the reliability of CFEs into a proxy for drift control. Users still receive familiar suggestions (\"increase income by 65k\"), but the underlying screening process creates an interpretive layer: spikes in the dashboard metric \"drifted points rejected\" signal when CFEs become untrustworthy due to underlying drift or imbalance, while the \"class-balance ratio after acceptance\" reveals whether corrective actions reinforce equity. Thus, the very sequence of counterfactual interactions evolves into a real-time narrative, exposing whether fairness is sustainably maintained as data shifts. [PERSON] and [PERSON]'s work operationalizes [PERSON]'s core principle--using explainability artifacts as drift sensors--but extends it into the dynamic context of user feedback, adding metrics that translate abstract drift interpretation into actionable operational intelligence. The fil rouge unifying these works is the transformation of explainability from a post hoc illustration into the primary mechanism for diagnosing fairness drift. Both frameworks reject the separation of \"detect then explain\"; instead, interpretable outputs are the drift monitoring system. [PERSON] et al. achieve this through longitudinal contrasts of model internals (feature attributions and rule structures), making drift interpretable as evolving trade-offs and logical conditions. [PERSON] and [PERSON], recognizing the limitations of periodic model snapshots, extend this into the continuous flow of deployment by leveraging user interactions.