# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 1 Introduction  
In recent years, with the rapid growth of artificial intelligence (AI) and machine learning (ML), the concepts explainability and interpretability have taken on an increasingly central role, becoming in effect a hot topic. This growing attention has led to the development of two related but distinct branches of AI research: explainable AI (XAI), which refers to techniques that provide external, often post hoc, explanations for the behavior of complex black-box models, and interpretable AI, which focuses on models that are inherently understandable by human users due to their transparent structure. Although the terms are sometimes used interchangeably in the literature, in this review we follow the more precise distinction formalized in Section 2.1 (Definitions 5 and 6). This clarification is critical, particularly when analyzing how these two approaches intersect with concept and data drift. In this review, our focus is placed specifically on how both explainability andinterpretability contribute to understanding, monitoring, and adapting to drift phenomena. Given the rapid changes in areas like IoT, healthcare, etc., researchers are increasingly interested in uncovering the underlying reasons for these drifts, enabling more effective adaptation and more reliable AI systems. Since interest in this field has surged only in the last few years, there is a clear lack of systematic literature reviews (SLRs) addressing these concerns. This review aims to fill that gap with a clear distinction and taxonomy of explainability and interpretability techniques, evaluated in the context of drift. The few existing surveys are not systematic literature reviews, but rather more traditional literature reviews. There are eight such works in total: one is in Chinese [1], which makes it less accessible to a broader audience, and four others focus predominantly on the medical domain [2; 3; 4; 5]. In contrast, [PERSON] and [PERSON] provided a review of the literature on available techniques for detecting existing deep learning approaches, bridging research gaps in anomaly detection for IoT surveillance systems [6]. However, it provides no studies related to explainability or interpretability, explicitly stating that these aspects should be investigated in the future. Similarly, both [PERSON] et al. and [PERSON] et al. gave out detailed discussion on the concept of drift, not focusing on explainability or interpretability [7; 8]. Therefore, the aim of this SLR is precisely to fill this gap. This SLR seeks to provide a taxonomy that describes and enumerates all current strategies, methodologies, and models in the scientific landscape for addressing this topic. To the best of our knowledge, this is the first review that presents comprehensive tables consolidating the results of the various models and benchmarks found in the highly fragmented literature. Our hope is that this collected and distilled knowledge will serve as a valuable resource and reference for researchers, equipping them with a consultable tool to make the right choices based on their specific circumstances. In this review, we pose two complementary sets of research questions. The first set, General Questions (GQs), gives a high-level view of the literature, whereas the second set, Specific Questions (SQs), drills down into the technical details of the proposed approaches. The General Questions are  
**GQ1. **: **Trend**: How has research on explainability and interpretability for concept and data drift evolved over time? **GQ2. **: **Domain**: Which application areas most frequently adopt these techniques? **GQ3. **: **Publication Venues**: Which journals, conferences, or workshops exert the greatest influence in this field? **GQ4. **: **Research Groups**: Which institutions or teams are the most prolific contributors? The Specific Questions (SQs) are  
**SQ1. **: **Online vs. Offline Learning**: How do online learning approaches differ from offline (batch) learning when coupled with explainability? **SQ2.