Explainable AI helps build trust in machine learning systems by providing insights into how models make decisions. This is particularly important in high-stakes domains such as healthcare, finance, and criminal justice, where the consequences of incorrect or biased decisions can be severe. When users understand how a model arrives at a particular output, they are more likely to trust and rely on the system [5; 6]. Explanations can help identify errors, biases, and unexpected behaviors in machine learning models. Developers can debug and improve their models by understanding how features influence predictions, leading to more accurate and reliable systems [7; 8]. In some domains, there are legal and regulatory requirements to explain algorithmic decisions. For example, the European Union's General Data Protection Regulation (GDPR) includes a \"right to explanation\" for individuals subject to automated decision-making. Explainable AI techniques can help organizations comply with these regulations [9; 10]. Explainable AI can also help uncover biases and unfairness in machine learning models [11; 12]. Furthermore, explainable AI enables effective human-AI collaboration by providing a common understanding between humans and machines [13; 14]. Several approaches have been proposed to explain black-box models, ranging from local methods that provide explanations for individual predictions to global methods that aim to capture the model's overall behavior. Post hoc explanations, such as LIME (Local Interpretable Model-Agnostic Explanation) [6], SHAP (Shapley Additive Explanation) [15], and Grad-CAM (Gradient Weighted Class Activation Mapping) [16], have gained popularity in recent years as a way to explain black-box models by perturbing the input data and learning a surrogate model that approximates the original model's behavior locally. Although these methods can effectively generate explanations, they have been criticized for several reasons. First, the explanations may not reflect the true mechanisms of the original model but rather a simplified version that is easier to interpret [17]. Second, the surrogate model may not be faithful to the behavior of the original model in some cases, leading to potentially misleading explanations and being open to adversarial attacks [18]. Third, the perturbation of input data can alter the features' semantics, rendering the explanations invalid or misleading and creating unstable explanations that arise with models already trained [19; 20]. To address these limitations, some researchers have proposed using inherently interpretable models, such as decision trees or linear models, instead of black-box models for high-stakes decision-making [21]. However, this approach may come at the cost of reduced prediction accuracy, as interpretable models may not be able to capture the complexity of some datasets as well as black-box models. Moreover, this approach cannot be applied to models that are already deployed and running. Replacing existing black-box models in production with interpretable models requires re-training the whole model from scratch, which can be resource-intensive and time-consuming. In our previous works [22; 23], we proposed two novel approaches to improving the global explainability of black-box models, which we call _pre hoc explainability_ and _co hoc explainability_. Our approach aims to incorporate explanations derived from an inherently interpretable white-box model into the original model's learning stage without compromising its high prediction accuracy. Instead of learning a post hoc white-box model, our idea is to learn a white-box model that is explainable from the start and then let this explainer model guide the learning of the black-box predictor model. This approach aims to address the limitations of post hoc explanations, such as potential discrepancies between the explainer and the black-box model [24], and the computational overhead associated with generating explanations after model training [18].