# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 7 Research Questions
### Specific Questions
Conversely, statistically rigorous approaches (Change-Point Theory (C2), Bayesian Modeling (C3)) offer measurable uncertainty estimates yet struggle to translate \(p\)-values or posterior shifts into actionable domain narratives. This dichotomy is particularly problematic in safety-critical domains (e.g., healthcare, energy), where 63% of studies prioritize real-time adaptation but face computational bottlenecks when embedding Shapley-based attribution (Feature Attribution and Game Theory (C6)) or fuzzy rule updates (Fuzzy Transparency (C8)). Second, evaluation methodologies remain fragmented and domain-specific. Only 6% of the papers (5/82) employ explicit interpretability metrics (e.g., \"faithfulness\" in Rule Mining (C8), \"robust validity\" in Graph Attention [C10]), while 94% rely on qualitative or visual assessments. This absence of standardized benchmarks impedes cross-class comparison--such as evaluating whether prototype migration (Prototype/Medoid Tracking (C9)) offers more actionable insights than causal dependency maps (Causal and Temporal Tests (C4)). Compounding this, dataset bias skews validation: Electricity (11 papers) and INSECT (7 papers) dominate evaluations, marginalizing image/text drift in classes like Dataset/Provenance (C15) and undersampling high-stakes domains (e.g., aviation: 1 paper). Third, temporal and human-factor limitations persist. While 73% of locally scoped methods (e.g., instance-level SHAP in C6) successfully pinpoint granular drift triggers, they risk overlooking systemic latent-space ruptures (C7) or long-term cyclical patterns. Only 11% of the papers address incremental/recurrent drift, as most rule-based systems (C5) discard historical clauses during adaptation, erasing context essential for domains like finance. Furthermore, 93% of the studies (76/82) target data scientists--neglecting interfaces for operational stakeholders who require plain-language drift narratives (e.g., clinicians interpreting Bayesian confidence shifts (C3) or plant managers acting on fuzzy rule updates (C8)). * **Future Directions:** Building on the systematic challenges identified, four interconnected research priorities emerge as critical for advancing explainable drift adaptation. Foremost, the field urgently requires unified evaluation frameworks capable of transcending domain-specific limitations. Such frameworks should integrate multimodal benchmarks--spanning healthcare sensor streams, financial transaction logs, and image/video sequences--annotated with ground-truth drift rationales (e.g., documented causal events like equipment failure or regulatory changes). These must be paired with standardized quantitative metrics that capture both narrative fidelity (assessing consistency between geometric explanations in C1 and rule-based interpretations in C5) and operational actionability (measuring reductions in mitigation latency when stakeholders act on explanations, such as clinicians adjusting triage protocols after reviewing attribution dashboards in C6). Concurrently, methodological innovation should focus on synergistic integration across taxonomic classes. Embedding causal dependency analysis (C4) within real-time attribution systems (C6) could elucidate why features shift--for instance, revealing that \"loan approval delays stem from concurrent workload spikes (employee E203) and revised compliance thresholds.\" Similarly, fusing Bayesian uncertainty tracking (C3) with compliance provenance tagging (C15) would generate auditable drift logs for regulated domains, such as FDA-mandated model versioning in healthcare diagnostics. To bridge human-centric gaps, future work must prioritize scalable collaboration mechanisms between stakeholders and AIsystems. This entails developing \"explanation distillation\" techniques that convert computationally intensive outputs--like Shapley value arrays (C6) or latent-space trajectories (C7)--into domain-specific narratives (e.g., \"pump P402 degradation signaled by medoid migration from quadrant Q2 \(\rightarrow\) Q4\" for industrial engineers).