Formulating a Theoretical Framework for Deep Learning Model Comprehensibility

Abstract

This Deep learning models have achieved remarkable success across various fields. However, their 'black box' nature has become a fundamental issue. Explainability aims to bridge this gap by providing insights into the decision - making processes of models. This paper delves into the theoretical foundations of explainability in deep learning, focusing on mathematical and conceptual aspects.We examine the limitations of current explainability approaches and discuss how interdisciplinary methodologies can enhance our understanding of deep learning systems. Moreover, we explore the potential of integrating explainability with robustness, fairness, and generalization to create more reliable AI systems.The paper also highlights several challenges, such as the trade - off between interpretability and predictive power, the scalability of explainability methods, and the lack of standard evaluation metrics. Furthermore, we propose novel research directions, including topological analysis, causal reasoning, and probabilistic explainability models.Particular attention is given to the role of human cognition, decision - theoretic frameworks, and the use of explainability as a tool to improve the reliability of deep learning models in high - stakes scenarios. We also investigate how explainability techniques can enhance the deployment and optimization of deep learning models in real - world environments, ensuring their ethical and practical applications.This work aims to provide a comprehensive framework for improving the transparency, interpretability, and accountability of AI - driven decision - making systems.