# Explainable Artificial Intelligence: Advancements and Limitations
## 3 Overview of XAI Categorisation: Key Dimensions  
This section explores six critical dimensions for categorising XAI methodologies: explanation phase, explanation level, model specificity, target audience, granularity, and modality of explanations. These dimensions provide a structured framework for understanding how XAI techniques differ in their approaches and applications; please also refer to Figure 1 for an overview. Additionally, Table 1 presents the categories of some well-known XAI methodologies along these dimensions, and it can be used as a guide when choosing the right technique for a given task. **Explanation Phase. ** XAI techniques are typically categorised into two main types: post hoc and ad hoc methods. Post hoc methods are applied after the model has been trained and offer explanations without altering the model structure or any training/test time intervention [63, 68]. These methods aim to interpret the decision-making process retrospectively, often using visualisations or perturbation techniques. In contrast, ad hoc methods refer to models that are either inherently interpretable, such as linear models and decision trees, or that incorporate explainability directly into their architecture during training [66, 69]. While inherently interpretable methods often have limited predictive capacity, techniques that integrate explainability into black-box models typically experience a performance trade-off due to the structural training/test time interventions. Unlike post hoc methods, which provide explanations after the fact, ad hoc techniques offer immediate by-product interpretability. Figure 1: XAI dimensions for various applications. \begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c|c} \hline \multirow{2}{*}{**Methodology**} & \multirow{2}{*}{**Phase**} & \multicolumn{2}{c|}{**Level**} & \multicolumn{2}{c|}{**Specificity**} & \multicolumn{2}{c|}{**Audience**} & \multicolumn{2}{c|}{**Granularity**} & \multicolumn{2}{c|}{**Modality**} \\ \cline{2-11} \cline{3-12}  & **Post hoc** & **Ad hoc** & **Global** & **Local** & **Specific** & **Agnostic** & **Researchers** & **End Uses** & **Fine-Grained** & **Coarse-Grained** & **Visual** & **Conceptual** \\ \hline Activation maximintion [61,70,71,72] & ✓ & � & � & ✓ & � & ✓ & � & ✓ & ✓ & � & ✓ & ✓ & � \\ Saliency mapping [60,67,73] & ✓ & � & � & � & ✓ & ✓ & � & ✓ & ✓ & � & ✓ & ✓ & � \\ Integrated Gradients [74] & ✓ & � & � & ✓ & ✓ & � & ✓ & ✓ & � & ✓ & ✓ & ✓ & � \\ Multilevel XAI [55] & � & ✓ & � & ✓ & ✓ & � & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ CBM [67] & � & ✓ & � & ✓ & ✓ & � & ✓ & ✓ & ✓ & � & � & ✓ \\ CAM [76] & ✓ & � & � & ✓ & ✓ & � & ✓ & � & � & ✓ & ✓ & � \\ LIME [77] & ✓ & � & � & ✓ & � & ✓ & ✓ & ✓ & � & ✓ & ✓ & � \\ Anchors [78] & ✓ & � & � & ✓ & � & ✓ & ✓ & ✓ & � & ✓ & ✓ & � \\ Network dissection [79] & ✓ & � & ✓ & � & ✓ & � & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ TCAVs [66] & ✓ & � & ✓ & � & ✓ & � & ✓ & ✓ & ✓ & � & � & ✓ \\ Post hoc CBM [69] & ✓ & � & ✓ & � & ✓ & � & ✓ & ✓ & ✓ & � & � & ✓ \\ \hline \end{tabular}
\end{table} 
Table 1: Categories of some well-known XAI methodologies. **Explanation Level. ** XAI methods can also be distinguished by whether they provide global or local explanations. Global explanations aim to give a comprehensive view of how a model behaves across a wide range of inputs, offering insight into the overall functioning of the model. Local explanations, in contrast, focus on specific predictions, providing details on why the model makes a certain decision for a particular input. In short, this category highlights the scale at which a model's behaviour is interpreted, whether explaining its behaviour in general or for individual instances. **Model Specificity. ** For this aspect, XAI methods are labelled as either model-specific or model-agnostic. Model-specific methods [67; 76; 80; 81] are designed to work with particular types of architectures, such as CNNs, utilising their unique architecture for explanations.