Interpreting Deep Learning Models through Symbolic Reasoning

Abstract

This paper explores a novel approach to interpreting deep learning models by integrating symbolic reasoning. Traditional deep learning models are often considered "black boxes," making it difficult to understand their decision-making processes. This work proposes a framework that combines the strengths of both deep learning and symbolic AI. Specifically, we leverage the predictive power of deep neural networks alongside logical inference techniques to explain the internal reasoning of these models. The core mechanism involves comparing the outputs of a deep learning model with knowledge represented in a symbolic knowledge base. By applying logical inference algorithms, we can derive a symbolic representation of the model's reasoning process, providing a more transparent and understandable explanation. This approach aims to enhance the trustworthiness and interpretability of deep learning systems, a critical step towards wider adoption and reliable deployment. The key contributions include a novel framework and a methodology for translating deep learning outputs into logical inferences, leading to enhanced model interpretability.