## 4 Discussion  
While GE is primarily used in the field of genetic programming, it can also be relevant in the area of XAI due to its ability to generate human-readable and interpretable solutions that resemble the human way of thinking. Grammars can restrict the structure and components of possible explanations and indicate causality. GE can provide functional representations of the model that mimics the decision-making process. These representations can be analyzed by humans in order to gain insights into the model structure and way of working and, thus, to allow for better understanding of the behavior of the AI system. The presented experiments point to the necessity of including more complete external declarative knowledge in building reliable and trustworthy AI models. The KC can help to provide context information and include domain-specific expertise. By incorporating this knowledge, models can better capture the problem and provide better predictions. Incorporating external knowledge can enhance the model's robustness against uncertainties, incomplete information, noisy data, and hidden nonlinearities. Moreover, the use of expert knowledge can aid in developing solutions that are robust and generalize well to new data and diverse scenarios. One of the major disadvantages of the GE approach is the problem with convergence and reproducibility of the results. A GA does not guarantee finding optimal/any solutions in all cases. The search space of many real-world problems is vast and complex, making it difficult to guarantee finding the optimal solution. Selected experiments show that different runs of GE algorithms can return different functional models as explanations, especially for classification problems. Moreover, despite the demonstrated efficacy of grammatical evolution, the method can sometimes become trapped in local minima of the objective function, leading to performance that may fall short of expectations. To address these challenges, hybrid techniques combining local minimization algorithms with grammatical evolution can be employed. However, as grammatical evolution involves integer optimization, these techniques must be specifically tailored to suit this framework [39]. Context-free grammars can be employed to constrain the structure of an explanation model. However, a growing demand for penalization of complexity and redundancy within a model has arisen. Currently, there are no mechanisms to verify if the proposed genome includes superfluous information, making it challenging to eliminate unnecessary components. Additionally, estimating the number of building blocks necessary for a functional model a priori remains difficult. At this moment, numerical threshold optimization is not performed effectively by the GE algorithm and is strongly dependent on user-specified numerical values. Future works should involve the implementation of solutions for optimal cutoff point searches. Ensuring ethical and legal compliance necessitates the incorporation of external knowledge. Models must align with ethical guidelines, legal regulations, and privacy standards. By including an external knowledge component that addresses these elements, models can be constructed focusing on reliability and responsible decision making. Our proposal introduces a novel application of GE in the area of XAI called model-driven explainable artificial intelligence. This approach transcends the limitations of traditional 'black-box' AI models by providing a transparent, interpretable framework that resembles human reasoning. By using GE for function identification, we not only enhance the technical capabilities of AI but also bring it closer to human thinking patterns, making AI decisions more understandable and trustworthy. In the proposed approach, i.e., MD-XAI, there is a visible tradeoff between the simplicity and interpretability of the model and its accuracy.