## 5 Conclusions  
The presented experiments demonstrate that relying solely on shallow explainability techniques can result in incorrect models and misleading or inconsistent explanations. The discrepancies in explanations highlight the inherent limitations of the abstract, model-agnostic approach. Whenever feasible, simple models and explanation techniques that foster a deep understanding of the model's structure and behavior should be preferred. A promising technology for advancing and expanding the identification of interpretable functional structures is grammatical evolution. GE can be effectively used to identify functional dependencies within data. The presented experiments highlight the benefits of generating an accurate model with a small amount of training data compared to shallow methods, showcasing its potential in creating interpretable AI models and enhancing transparency. Models created using GE, even with limited amounts of training data, can surpass those developed by more traditional, shallow techniques. The adaptability of GE to different data types and its capacity to reveal complex patterns underline its robustness and versatility. This study underscores the importance of the 3C principle (Causality, Components, and strutCture) in enhancing the interpretability and effectiveness of model-driven XAI. This principle serves as a guideline for the construction of transparent and understandable AI models. By emphasizing causality, identifying crucial system components, and elucidating their interconnections, the 3C principle facilitates a deeper understanding of the underlying mechanisms of AI decisions. Adopting this principle not only advances the development of explainable models but also helps to build trust and reliability in AI systems. The significance of meaningful intermediate variables is also paramount in the construction of explainable AI models. MIVs, by providing an additional layer of abstraction, help in reducing the complexity of data models while retaining essential information that contributes to the interpretability of the AI decision-making process. These variables enable the encapsulation of complex relationships within the data into more understandable and tangible concepts, thereby facilitating a more intuitive analysis and explanation of AI models. Collaboration between computational techniques and external knowledge (human expertise) is critical to the advancement of ethical and responsible AI, ensuring that models are aligned with real-world applicability and constraints. By bridging the gap between raw data and high-level interpretations, MIVs play a crucial role in demystifying AI operations and enhancing the accessibility of AI insights for domain experts and other users of AI systems. This study contributes to the field of XAI by offering a novel approach that bridges the gap between the predictive power of machine learning and the human need for understanding and trust in AI systems. The insights gained herein pave the way for future research efforts aimed at creating AI models that are not only powerful but also transparent and in line with ethical standards. Future work should focus on enhancing the efficiency and scalability of the MD-XAI approach. One critical aspect under consideration could be the reduction of computational complexity, which remains a significant challenge with respect to the broader adoption and application of these methodologies. Efforts could be directed towards optimizing the grammatical evolution process and the selection of MIVs to ensure that models can be developed and interpreted with reduced computational resources, making the technology more accessible for large-scale and real-time applications. Conceptualization, D.S. and A.L. ; methodology, D.S. and A.L. ; software, D.S. ; validation, D.S. and A.L. ; formal analysis, D.S. and A.L. ; investigation, D.S. and A.L. ; resources, D.S. ; data curation, D.S. ; writing--original draft preparation, D.S. and A.L. ; writing--review and editing, D.S. and A.L. ; visualization, D.S. ; supervision, A.L.