# 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 following presents a selection of the most prevalent explainable AI techniques that can be applied to both local and global contexts. While this overview encompasses widely adopted XAI methods identified in the literature, it is not exhaustive, as comprehensive coverage of all explainability approaches would extend beyond the intended scope of this section. All the example charts here, except for LIME, are taken from [PERSON]'s book [21]. * **SHAP** (Shapley Additive Explanations): This method assigns each feature an importance value derived from Shapley values in cooperative game theory. The model's prediction is treated as a \"payout\" fairly divided among features, ensuring that attributions sum up precisely to the difference between the actual and baseline outputs. It supports both global and local explanations. Figure 7 shows a SHAP summary plot for a random forest classifier trained with 100 trees to predict the sex of penguins. Every dot in the figure is a Shapley value; its vertical position marks the feature, its horizontal position represents the size and direction of that feature's contribution for one bird, and its color encodes the raw feature value from low (blue) to high (red). The cloud of points therefore conveys two things simultaneously: the importance ranking (features are ordered from top to bottom by the spread of their dots) and the effect pattern (for example, higher body-mass values cluster on the negative side, showing that heavy birds push the model away from \"female\"). Because overlapping dots are jittered vertically, the reader also gets a sense of the distribution of effects within each feature. In one compact visual, novices can see that body_mass_g dominates the decision process, followed by bill_depth_mm and bill_length_mm, and can infer how low versus high measurements shift the predicted probability [21]. * **LIME** (Local Interpretable Model-agnostic Explanations): This method explains individual predictions by training a simple surrogate model locally around the instance. This surrogate approximates the black box locally, highlighting influential features for a specific prediction. LIME has been designed to be applied locally. In Figure 8 this procedure is illustrated for a random forest classifier trained on a Diabetes dataset. The forest assigns a 0.73 probability of diabetes to a particular patient. LIME perturbs that patient's eight clinical attributes (glucose, BMI, age, etc. ),  
Figure 7: Shap example. reevaluates each variant with the forest, and fits a weighted linear surrogate whose \(R^{2}\) against the forest's outputs is 0.92--evidence of high local fidelity. The bar chart shows that elevated glucose and body mass index are the dominant positive contributors, while a slightly below-average blood pressure exerts a small negative influence [17]. * **Partial Dependence Plots (PDP):**These show the marginal effect of varying one or more features on the model output while averaging over the joint distribution of all other features. The resulting curve (or surface) visualizes whether the relationship is linear, monotonic, or more complex. PDPs assume the features being varied are independent of the others; violations can lead to misleading interpretations. They provide a global view. Figure 9 illustrates the idea for a random forest regressor trained to predict the number of bikes rented on a given day. Each panel depicts the partial dependence of the prediction on a single weather variable. The left-hand plot shows temperature: predicted rentals climb sharply as the day warms, level off at around 20 \({}^{\circ}\)C, and dip slightly beyond 30 \({}^{\circ}\)C, indicating that the forest has learned that very hot days discourage cycling. The middle plot varies humidity and shows a flat response up to roughly 60%, after which predicted demand falls--consistent with would-be cyclists being deterred by muggy conditions.