# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 2 Background: Drift Phenomena and Explainable/Interpretable AI
### Explainable AI
#### 2.3.2 Classical Approaches
The first split is on cnt_2d_bfr (rentals two days earlier); when this lagged count exceeds 4570, the model anticipates higher demand, and a subsequent split on temperature (>13 \({}^{\circ}\)C) refines the prediction further. The four terminal nodes are summarized by box-plots: moving from the top-left panel (cool day, low prior demand) to the bottom-right panel (warm day, very high prior demand), the median predicted rental count climbs sharply, visualizing in one glance how the two variables interact inside the SVM. By walking each path, a reader can translate the black-box logic into plain IF-THEN statements, while the overall structure of the tree conveys which features the SVM relies on most [21]. * **Anchors**: The model learns concise IF-THEN rules (anchors) for a target instance such that, with high empirical precision, any sample satisfying the rule yields the  
Figure 11: PI example. same prediction. Precision and coverage statistics accompany each rule, giving users a quantified guarantee of stability. Anchors supply local explanations centered on the rule's coverage region. Figure 13 makes this concrete for a random forest classifier built from the bike rental dataset. The regression target (daily rental count) has been binarized into above versus below the trend line, and the forest is asked to justify six individual predictions. For every case, the anchor algorithm fixes one or two feature predicates--here mainly temperature buckets such as temp \(\epsilon[14,21]\), occasionally augmented by weather = bad--and then generates synthetic neighbors by sampling the remaining features from the training set. If at least 95% of those neighbors inherit the forest's original label, the predicate set is accepted as an anchor. In the plot each horizontal bar lies on a precision axis from 0 to 1: the right-hand end of every bar touches or exceeds the 0.95 threshold, signaling that the discovered rule is highly faithful. The bar thickness encodes coverage; the thickest rules apply to over 20% of comparable days, while thinner bars cover only a sliver of the data space. That most anchors rely solely on temperature confirms, in an immediately readable way, that the classifier's decision boundary is dominated by this single variable, with bad weather entering the rule set only when it is needed to secure high precision [21]. * **Counterfactual Instances**: The algorithm searches for the minimum change(s) to the input features that would flip the model's prediction to a specified target class. The optimization balances prediction flip, proximity to the original instance, and optional feasibility constraints. The resulting counterfactual highlights actionable levers and their associated \"cost.\" Counterfactuals are inherently local. Tables 1 and 2 illustrate the idea for a radial-basis SVM trained on the German Credit dataset. The customer described in Table 1 receives only a 24.2% probability of good credit. A multi-objective counterfactual search then generates ten nondominated alternatives, reported in Table 2. Every viable solution shortens the loan duration from 48 months to roughly 23 months; many also upgrade the job category from \"unskilled\" to \"skilled,\" and several switch the recorded gender from female to male. All ten counterfactuals raise the predicted probability above the 50% threshold, but they differ  
Figure 13: Anchor example. in how many features they modify and how far they stray from the training manifold, offering a spectrum of trade-offs between realism and efficacy. The repeated gender flip, coupled with large probability jumps, additionally exposes a bias latent in the SVM--an insight that would remain hidden without counterfactual analysis [21].