To accomplish this goal, we design two different frameworks: (1) a Pre Hoc Explainable Framework, where the white-box model regularizes the black-box model for optimizedfidelity, and (2) a Co Hoc Explainable Framework, where the white-box and black-box models are optimized simultaneously with a shared loss function that enforces fidelity. In the Pre Hoc Explainable Framework, we first train the explainer model \(g\) and then use it to guide the learning of the predictor model \(f\). The objective function for training the predictor model includes a fidelity term that minimizes the distance between the predictor's and explainer's outputs, encouraging the predictor to mimic the explainer's behavior. This approach ensures that the predictor model is regularized by the explainer model, leading to improved interpretability. In the Co Hoc Explainability Framework, we jointly optimize the predictor model \(f\) and the explainer model \(g\) during training. The shared loss function consists of both models' standard supervised learning objective (e.g., cross-entropy loss) and a fidelity term that minimizes the distance between their outputs. By simultaneously training both models, we encourage the predictor to learn from the explainer and the explainer to adapt to the predictor, resulting in a more coherent and interpretable system. Our proposed frameworks differ from existing approaches in several aspects. First, we integrate interpretability directly into the model training process rather than relying on post hoc explanations. Second, we use a transparent white-box model to guide the learning of the black-box model, ensuring that the explanations are faithful to the predictor's behavior. Finally, our frameworks are model-agnostic and can be applied to any differentiable predictor and explainer models. The main contributions of this paper are as follows:  
1. We propose two complementary frameworks for integrating explainability into black-box model training: (i) pre hoc explainability, where a white-box model regularizes the black-box model, and (ii) co hoc explainability, where both models are jointly optimized. These frameworks ensure faithful explanations without post hoc computation overhead and maintain model accuracy. 2. We extend our frameworks to provide both global and local explanations by incorporating Jensen-Shannon divergence with neighborhood information. Our two-phase approach generates instance-specific explanations that are more stable and faithful than post hoc methods like LIME, while being 30\(\times\) more computationally efficient at inference time. 3. We demonstrate through extensive experiments that our frameworks not only improve explainability but also enhance white-box model accuracy by up to 3% through co hoc learning. This finding is particularly valuable for regulated domains like healthcare and finance where interpretable models are mandatory. The remainder of this paper is organized as follows: Section 2 reviews related work in explainable artificial intelligence. Section 3 presents our proposed pre hoc and co hoc explainability frameworks and their theoretical foundation and describes our experimental setup and evaluation metrics. Section 4 presents and discusses the results of our experiments. Finally, Section 6 concludes the paper.