# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 2 Background: XAI Taxonies  
The primary focus of this review is on the recent applications of XAI across various domains. However, to fully appreciate how XAI has been implemented in these areas, it is essential to provide a brief overview of the key taxonomies of XAI methods. While an exhaustive discussion of these taxonomies, along with the advantages and disadvantages of each method, lies beyond the scope of this article, a concise summary is necessary to ensure that the content and findings of this review are accessible to a broad audience. For those seeking a more comprehensive exploration of XAI taxonomies and detailed discussions on the pros and cons of various XAI methods, we recommend consulting recent reviews [5; 9; 10; 11] and comprehensive books on the subject [12; 13]. Generally, XAI methods can be categorized based on their explanation mechanisms, which may rely on examples [14; 15; 16], counterfactuals [17], hidden semantics [18], rules [19; 20; 21], or features/attributions/saliency [22; 23; 24; 25]. Among these, feature importances are the most common explanation for classification models [26]. Feature importances leverage scoring and ranking of features to quantify and enhance the interpretability of a model, thereby explaining its behavior [27]. In cases where the model is trained on images, leading to features representing super pixels, methods such as saliency maps or pixel attribution are employed. Evaluating the saliency of features aids in ranking their explanatory power, applicable for both feature selection and post-hoc explainability [5; 28; 29]. Other approaches to categorizing XAI methods are related to the techniques applied, such as (i) ante-hoc versus post-hoc, (ii) global versus local, and (iii) model-specific versus model-agnostic (see Figure 1). Ante-hoc/intrinsic XAI methods encompass techniques that are inherently transparent, often due to their simplistic structures, such as linear regression models. Conversely, post-hoc methods elucidate a model's reasoning retrospectively, following its training phase [5; 26; 30]. Moreover, distinctions are made between local and global explanations: while modular global explanations provide an overarching interpretation of the entire model, addressing it comprehensively, local explanations elucidate specific observations, such as individual images [31; 32]. Furthermore, explanation techniques may be categorized as model-specific, relying on aspects of the particular model, or model-agnostic, applicable across diverse models [5; 33]. Model-agnostic techniques can be further categorized into perturbation- or occlusion-based versus gradient-based. Techniques like occlusion- or perturbation-based methods manipulate sections of input features or images to generate explanations, while gradient-based methods compute the gradient of prediction (or classification score) concerning input features [34]. As with machine learning models themselves, there is no universally best XAI approach; the optimal technique depends on factors such as the nature of the data, the specific application, and the characteristics of the underlying AI model. For instance, local explanations are particularly useful when seeking insights into specific instances, such as identifying the reasons behind false positives in a model's predictions [35]. In cases where the AI model is inherently complex, post-hoc techniques may be necessary to provide explanations, with some methods, like those relying on gradients, being applicable only to specific models, such as neural networks with differentiable layers [34; 36]. While a variety of XAI methods are available, evaluating their effectiveness remains a less-explored area [4; 11]. As illustrated in Figure 1, XAI evaluation approaches can be categorized into consultations with human experts, anecdotal evidence, and quantitative metrics. As explained above, our review extends existing work on XAI methods and taxonomies [5; 9; 10; 11] by shifting the focus towards the practical applications of XAI across various domains.