# Explainable Artificial Intelligence: Advancements and Limitations
## 3 Overview of XAI Categorisation: Key Dimensions
Model-agnostic methods [40; 66; 77; 78; 82], however, can be applied to any ML model, regardless of its structure, as they only require test inputs and the prediction function. This categorisation reflects the adaptability of XAI techniques, indicating whether they are specialised for certain architectures or can be broadly applied across different models. **Target Audience. ** XAI methods are designed to cater to different groups of users with varying needs. Some tools are tailored for researchers and developers who require deep insights into model behaviour to refine and improve model architectures. These tools often provide detailed, technical explanations and are useful during model development. On the other hand, there are methods aimed at end users who need to understand model outputs without delving into the underlying technical details. For these users, interactive visualisations or simplified explanations are more appropriate, helping them trust and comprehend a system's decisions. Google's what-if tool [83], AI Explainability 360 toolkit by IBM [84], and H2O AutoML platform [85] are some user-friendly examples. **Granularity. ** Granularity refers to the level of detail provided by an XAI technique. In this direction, methodologies can be categorised as offering fine-grained or coarse-grained explanations. Fine-grained explanations, such as concept-level insights, delve into more detail of a model's decisions, offering feature-specific information [75; 79]. In contrast, coarse-grained explanations, like class-level or object-level insights, offer a more general understanding of why a model made a particular prediction, typically explaining what contributed to the decision overall [63; 68; 80]. This category helps determine the level of precision or detail in the explanations, depending on the needs of the user. To give an example, for an animal classification task, a coarse-grained explanation would be a saliency map highlighting the class of interest for a test image, whereas a fine-grained explanation would generate more granular, concept-wise heatmaps highlighting different parts of the object in an input image alongside their textual descriptions. **Modality of Explanations. ** In this aspect, the distinction lies between the types of generated explanations. For visual tasks, there are two common modalities: visual and conceptual. Visual explanations often involve heatmaps that highlight important regions of an image, giving an intuitive, visual representation of what influenced the model's decision [40; 77]. Conceptual explanations, on the other hand, ideally offer explanations as human-understandable concepts, e.g., _stripe_ for a _zebra_ image [66; 67]. In the next sections, we delve deeper into these two modalities, offering more detailed insights and a review of the relevant literature surrounding both modalities. An overview of the next section is also presented in Figure 2. For each modality, we explore key methodologies, drawing on existing research to highlight the strengths and limitations of these approaches. This examination also aims to provide a comprehensive understanding of the role each modality plays in enhancing AI interpretability and trustworthiness.