# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 7 Research Questions
### Specific Questions
Datasets and Benchmarks:** Datasets utilized in each taxonomic class were examined, revealing that no single dataset can be regarded as a definitive benchmark for drift explainability and interpretability. Various datasets (images, numerical, textual) appear across studies, with Electricity (11 papers), INSECT (7 papers), CoverType (5 papers), and Airlines (5 papers) being the most common. However, these four datasets cannot be considered standard benchmarks, resulting in poor reproducibility and low standardization. Additionally, the systematic review highlighted an absence of established metrics, further confirming the current lack of benchmark standardization in the literature. * **SQ4. Models:** The taxonomic structure inherently encodes this answer. Each of the fifteen classes corresponds to a distinct technical or methodological foundation that the literature uses to make drift both detectable and explainable. Together, these classes reveal five macro-families: Statistical and Probabilistic, Feature Importance and Attribution, Evaluation and Auditing, Logic and Rule-Based, and lastly, Interactive and Human-Centric. Statistical models still dominate, leveraging distance measures and hypothesis tests that are naturally decomposable for explanation. Representation-centric approaches offer the richest narratives, while symbolic and governance-oriented models provide human-readable rules and audit trails. * **SQ5. Metrics:** This systematic literature review reveals a profound scarcity of standardized quantitative metrics for evaluating explainability in concept and data drift. Only five explicitly defined interpretability metrics were identified across the entire corpus of 82 studies, with each metric appearing exclusively in a single paper. These isolated cases include user studies validating geometric visualizations in distance/divergence methods, faithfulness and monotonicity tests for rule-based systems, feature agreement scores in attribution frameworks, fidelity-sparsity trade-offs in graph-based approaches, and robust-validity measures for counterfactual recommendations. This extreme fragmentation--coupled with the complete absence of reused or benchmarked metrics--constitutes a fundamental limitation that undermines reproducibility, cross-method comparison, and scientific advancement in the field. Nevertheless, the taxonomy developed in this review uncovers richer, implicit evaluation paradigms that serve as de facto metrics within methodological families. Geometric interpretability--prominent in distance/divergence and latent-representation approaches--transforms statistical drift into visually verifiable narratives through spatial relationships like KL-divergence heatmaps or evolving decision boundaries, where interpretability is intrinsically tied to artifact clarity rather than formal scores. Uncertainty translation mechanisms central to Bayesian and fuzzy systems operationalize distributional shifts through domain-grounded stories, exemplified by decaying Dirichlet belief distributions in clinical models or sliding \(\gamma\)-factors in credit scoring, that transform confidence erosion into actionable operational narratives. Rule evolution tracking, characteristic of logical mining and fuzzy frameworks, treats syntactic modifications to rules as self-validating explanations, with the rate and magnitude of clause adaptations implicitly signaling drift severity. Operational alignment exemplified by resource optimization and compliance-tagging approaches reframes interpretability through efficiency trade-offs, such as configuration pairs encoding drift severity in computational feasibility boundaries or dataset versioning scores quantifying drift through provenance metadata. These patterns expose three interconnected barriers to metric standardization. Domain specificity fundamentally shapes evaluation criteria, as clinical contexts demand clinically actionable explanations, while cybersecurity prioritizes real-time attribution consistency.