## 1 Introduction  
Machine Learning (ML) has become a well-matured sub-discipline of Artificial Intelligence (AI) with a large collection of problem-solving tools and an extensive range of practical applications [1]. Domains such as biomedical data analysis, natural language processing, and large-scale technological systems have reported numerous successful projects and implementations [2, 3, 4, 5], along with significant progress regarding the variety of ML algorithms and tools. However, it appears that the traditional machine learning paradigm, e.g., as outlined in [6, 7], which focuses on developing a finite-set decision-making classifier or value prediction model, remains somewhat too restricted and conservative. There has been little to no conceptual progress observed concerning the problem formulation [8] within this framework. ML data repositories are typically based on the same simple scheme of attributive decision tables, without incorporating any other knowledge-based components. This seems to constitute a serious and inherent drawback when considering the need to comprehend the semantics of the induced models and justify, explain and validate the produced predictions and decisions. Especially when encountering some of the most challenging AI issues of today, which involve real understanding how intelligent systems function (e.g., model discovery for model-based reasoning [9], explainable artificial intelligence (XAI) [10, 11], trustworthy decision-making, and interpretable and explainable AI ([[https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability/](https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability/)]([https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability/](https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability/)) (accessed on 29 March 2024)),among others), the advantages of some model-driven approaches seem to be hard to overestimate. To advance towards the building of model-driven XAI, incorporating knowledge-based components and more advanced function identification methods (e.g., symbolic regression and grammatical evolution) appears necessary and promising. Explainable artificial intelligence (XAI) aims to deliver AI solutions, decisions, and predictions that are comprehensible to humans. Most current ML techniques, such as deep learning, rely on black-box models. To ensure an appropriate level of transparency, as well as justifiability and trustworthiness, it is crucial for humans to understand the underlying rules of the game. XAI includes both transparent models and post hoc explainability techniques. Transparent models (e.g., decision trees) are inherently interpretable (explainable by design), allowing direct insight into their decision-making processes. In contrast, post hoc techniques are focused on generating explanations based on the model output rather than its internal architecture. For example, some common strategies include generating visual explanations and employing surrogate models. Various taxonomies can be proposed for post hoc explainability techniques [10; 11; 12; 13; 14; 15; 16]. The most popular ones are listed as follows:  
* Local (explaining individual prediction) vs. global explanations (providing an overview of the model of the decision-making process);
* Model-agnostic (can be applied to any model, regardless of its internal workings) vs. model-specific explanations (customized for distinct model architectures). The current approaches to XAI often employ shallow models, which prioritize simplicity and interpretability over accuracy, constructing an easily comprehensible model on top of one derived from classical ML techniques. They operate on a subset of the original input data and lack the ability to integrate auxiliary knowledge. A broad and representative selection of such methods and tools is discussed in [10; 11]. Local Interpretable Model-Agnostic Explanations (LIMEs) [17] exemplify the concept of explanation by simplification.