## 2 Related Work
### Explanation Types and Evaluation  
XAI explanations can be categorized into several types, each offering different perspectives and serving distinct purposes. Rule-based explanations transform model decisions into human-readable rules, making them particularly valuable in domains requiring transparency, such as healthcare and finance [43; 44]. Decision trees exemplify this approach by breaking decisions into comprehensible if-then rules. While intuitive, these explanations may become unwieldy as model complexity increases. Feature-based explanations quantify the contribution of individual features to predictions [6; 15]. LIME and SHAP are prominent techniques in this category, with LIME approximating local behavior using interpretable models and SHAP assigning feature importance based on game theory principles. These methods provide valuable insights into which inputs most strongly influence outputs, enabling feature selection and bias detection. However, they may not capture complex feature interactions or non-linear relationships effectively [45]. Concept-based explanations bridge the gap between model internals and human understanding by expressing decisions through high-level concepts [20; 46]. Techniques like Concept Activation Vectors (CAVs) align model behavior with human-understandableconcepts, which is particularly useful in domains with established conceptual frameworks like medicine. These explanations facilitate communication with domain experts but require well-defined concepts and may not capture the model's full complexity. Instance-based explanations identify specific training examples that significantly influence predictions [7; 47]. Techniques like influence functions measure how individual training instances affect model outputs, while prototype selection identifies representative examples that characterize decision boundaries. These concrete explanations help users understand model behavior through familiar examples, though interpretation can be challenging when influential instances are not intuitively related to predictions. Local explanations focus on individual predictions, explaining specific decisions rather than overall model behavior [6; 15]. These targeted insights are crucial in domains where individual decisions carry significant consequences. Conversely, global explanations describe the model's general behavior across all instances [43; 48], providing a holistic understanding of the model's decision logic through interpretable surrogate models or feature importance methods. Multi-model explanations generate comparative views across different models [49; 50], helping users understand similarities and differences in how various models process information. This comparative approach provides insights into model robustness and generalizability, but can be computationally expensive and challenging to reconcile when models yield conflicting explanations. Selecting appropriate explanation types depends on factors including the target audience, model complexity, and application domain. In healthcare, concept-based explanations may better communicate with medical professionals, while rule-based explanations might be more suitable for patient-facing applications [51]. Different explanation types can be combined for comprehensive understanding--for example, aggregating local explanations to generate global insights [52]. Post hoc explainability can be evaluated using three key metrics: point fidelity, neighborhood fidelity [41], and stability [1].