## 6 Conclusions  
This paper introduces two novel explainability frameworks--pre hoc and co hoc explainability--that integrate interpretability directly into the training process of black-box machine learning models. Unlike post hoc methods that generate explanations after model training, our approach incorporates an inherently interpretable white-box model to guide the learning of the black-box model, ensuring that explanations are faithful to the model's behavior without compromising accuracy. The pre hoc framework uses a trained white-box explainer model to regularize the black-box predictor model through a fidelity term in the loss function, while the co hoc framework jointly optimizes both models with a shared loss function. Both frameworks leverage the Jensen-Shannon divergence to measure and minimize the discrepancy between the predictions of the two models, ensuring alignment in their behaviors. We further extend these frameworks to provide local explanationsby incorporating neighborhood information and developing a two-phase approach: first, training for global fidelity, then generating local explanations through fine-tuning the explainer model within the neighborhood of each test instance. This approach captures the local behavior of the black-box model, providing instance-specific explanations that are more relevant and accurate than global explanations alone.  
In conclusion, our pre hoc and co hoc explainability frameworks offer a promising direction for developing machine learning models that are both accurate and transparent. By integrating explainability directly into the training process and extending it to capture local behavior, our approaches address the limitations of post hoc methods and contribute to the advancement of trustworthy and interpretable AI systems.  
Conceptualization, C.A. and O.N.; formal analysis, C.A. and O.N.; methodology, C.A. and O.N.; software, C.A.; supervision, O.N.; validation, C.A. and O.N.; writing--original draft, C.A. and O.N.; writing--review and editing, C.A. and O.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NSF-EPSCoR-RII Track-1:Kentucky Advanced Manufacturing Partnership for Enhanced Robotics and Structures (Award IIP#1849213) and by NSF DRL-2026584.  
Not applicable.  
The datasets used in this study are publicly available. The HELOC dataset can be accessed on FICO's website ([[https://community.fico.com/s/explainable-machine-learning-challenge](https://community.fico.com/s/explainable-machine-learning-challenge)]([https://community.fico.com/s/explainable-machine-learning-challenge](https://community.fico.com/s/explainable-machine-learning-challenge)), accessed on 15 December 2023). The MovieLens 100k and 1M datasets are available through the GroupLens research lab at the University of Minnesota ([[https://grouplens.org/datasets/movielens](https://grouplens.org/datasets/movielens)]([https://grouplens.org/datasets/movielens](https://grouplens.org/datasets/movielens)), accessed on 15 December 2023).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AUC Area Under the ROC Curve BB Black-Box BCE Binary Cross-Entropy FM Factorization Machine HELOC Home Equity Line of Credit JS Jensen-Shannon KL Kullback-Leibler LIME Local Interpretable Model-Agnostic Explanation MAD Mean Absolute Deviation ROC Receiver Operating Characteristic SHAP SHapley Additive exPlanation TV Total Variation WB White-Box XAI eXplainable Artificial Intelligence