# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Dataset/Provenance and Compliance Tags
#### 6.15.1 Technical and Methodological Insights  
The \"Dataset/Provenance and Compliance Tags\" class represents a paradigm where the explainability of drift is not merely an auxiliary feature but is fundamentally engineered into the very artifacts and processes that handle data or model outputs. This approach ensures that the nature, origin, and implications of drift become transparent and immediately accessible to users, moving beyond simple detection to meaningful interpretation. [PERSON] et al. [109] exemplify this by reimagining dataset versioning. They embed a quantifiable measure of drift directly into the DOI identifier itself, repurposing the \"minor\" digit in the semantic versioning scheme to carry a PCA-based reconstruction-error score scaled between 0 and 100. This transforms the version string into an intrinsic interpretability tool: a value like \"1.42.0\" instantly signals significant divergence from reference data, while scores below 50 indicate negligible change. Crucially, this encoding happens automatically at the moment of dataset deposition, inseparably binding provenance (the version history) with explanation (the drift magnitude). Regulators or downstream users no longer need external dashboards; the answer to \"how different is this data?\" resides directly in the citation metadata they already encounter. The bounded scale further enhances interpretability by providing intuitive thresholds--scores nearing 100 correlate with transformative changes like variable rescaling, justifying a major version increment. Here, explainability is achieved through design artifactification: the version label becomes a self-contained narrative of change. [PERSON] et al. [43] extend this philosophy of embedded explainability into dynamic runtime environments, focusing on process-aware systems. Their innovation lies in augmenting a state-transition model--learned from historical logs--with synthetically generated \"constraint states\" derived from regulatory requirements. Each transition is explicitly tagged with a provenance flag indicating whether a prediction originates from empirical patterns in the training data or from a specific, previously unseen regulatory constraint (e.g., \"event D inferred from constraint c1\"). This mechanism provides immediate, plain-language explanations at prediction time, clearly distinguishing between familiar operational behavior and extrapolations mandated by new rules. The system outputs not just the predicted event and its probability but also this critical flag, allowing operators to instantly grasp whether the model is operating on known ground or navigating regulatory drift. Furthermore, [PERSON] et al. introduce a temporal dimension to explanation. Synthetic transitions start with a count of 1. As real-world data containing the once-hypothetical events arrives, their counts increase, and the \"constraint-only\" flag disappears. This evolving count structure visually narrates the assimilation of regulatory drift into operational norms, turning the transition graph itself into a dynamic ledger of compliance integration. Unlike post hoc explainers, their system intrinsically narrates drift by design, merging prediction with provenance-based explanation. While both papers share the core tenet of explanation-by-design, they diverge in scope and mechanism. [PERSON] et al. offer a static, snapshot-based interpretation suited to versioned datasets, providing a one-time, holistic assessment of the drift magnitude encoded in a persistent artifact (the DOI). [PERSON] et al., conversely, tackle the streaming, operational drift within processes, delivering granular, rule-specific explanations that dynamically evolve as new data validates regulatory extrapolations. The former quantifies drift through a single bounded metric (PCA error), prioritizing simplicity and immediate comprehension for data consumers.