Explainable Deep Learning Model Generation (EDLMG)

Abstract

This paper introduces a novel approach to generating explainable deep learning models, termed Explainable Deep Learning Model Generation (EDLMG). The core challenge in deploying deep learning models is their inherent lack of transparency, hindering trust and adoption. EDLMG addresses this by leveraging Generative Adversarial Networks (GANs) to automatically construct model architectures specifically designed for enhanced interpretability. Furthermore, it integrates rule-based reasoning to provide transparent explanations for the model's decision-making process. The system aims to create models that are not only accurate but also understandable, offering a crucial step towards responsible and reliable AI. The primary contribution lies in the automated generation of interpretable models, coupled with a robust explanation framework, thereby offering a practical solution to the explainability problem within deep learning. The framework incorporates key elements of controlled architecture design through GANs and subsequently employs symbolic reasoning for transparent decision justification. This approach allows for a shift from "black box" models to models where the underlying reasoning can be readily scrutinized and validated.