# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 7 Research Questions
### Specific Questions
Temporal misalignment arises when streaming-compatible methods requiring lightweight evaluation conflict with retrospective approaches enabling richer audits. Stakeholder divergence further complicates universal standards, with data scientists navigating mathematical constructs like Shapley values while end-users require linguistic outputs such as fuzzy rule modifications. Critically, this analysis suggests that effective metrics must bridge methodological families and operational contexts. A tiered framework emerges naturally from the taxonomy: foundational metrics should quantify core properties like attribution consistency across drift types; domain-specific adaptations must align with regulatory or safety imperatives; and temporal metrics could assess responsiveness in dynamic environments. Future progress hinges on developing hybrid evaluations that merge geometric clarity with rule fidelity, establishing standardized user studies transcending domain boundaries, and formalizing implicit paradigms such as uncertainty fidelity indices for Bayesian methods. The evidence synthesized through this systematic review yields four definitive conclusions regarding interpretability metrics for drift. First, metric scarcity remains endemic, with only around 6% of the papers employing explicit quantitative measures and no reuse across studies. Second, implicit evaluation paradigms dominate the landscape, substituting geometric, uncertainty, rule-based, and operational narratives for formal metrics. Third, meaningful standardization requires taxonomy-anchored innovation that bridges methodological families. Fourth, domain-temporal-stakeholder alignment constitutes a critical prerequisite for generalizable metrics. This comprehensive analysis transforms observed fragmentation into a research roadmap, positioning the proposed taxonomy as the foundational scaffold for metric development in explainable drift adaptation. * **Modustrial Adoption:** This research question closely relates to GQ2, highlighting how explainable and interpretable drift detection methods have been adopted primarily in sectors where interpretability is critical for ensuring reliability, safety, regulatory compliance, and operational efficiency. The most prominent examples of industrial adoption include healthcare, finance, energy, manufacturing, aviation, cybersecurity, and industrial process control. These domains are inherently sensitive to prediction errors, where incorrect or misunderstood model behavior could lead to severe consequences. However, despite widespread adoption, explicit empirical evidence detailing the benefits of these explainable drift detection methods, such as quantitative improvements in reliability, reduced maintenance costs, increased safety, or enhanced regulatory compliance, is not directly articulated in the reviewed papers. Instead, the implicit rationale for adoption relies heavily on perceived qualitative advantages, such as increased trust and improved decision-making clarity. * **Stakeholder Perception:** The audience targeted by each paper was analyzed during taxonomic evaluation. The results show 76 papers primarily targeting domain experts and data scientists and 15 papers targeting end-users. Users who benefit most from explainability and interpretability techniques for concept and data drift are primarily domain experts and data scientists (targeted by 76 out of 82 papers). This result strongly correlates with application domains characterized by high regulatory pressure, safety-critical operations, and significant accountability, such as healthcare, finance, energy, aviation, and manufacturing. In these sectors, stakeholders require detailed explanations to justify predictions, ensure regulatory compliance, and manage risks effectively. End-users (targeted by only 15 papers)benefit less frequently, predominantly in operational contexts like cybersecurity, recommendation systems, or general anomaly detection, where explanations are crucial for immediate corrective actions rather than compliance or extensive accountability.