Explainable Artificial Intelligence through Symbolic Reasoning Framework

Abstract

This paper proposes a framework for achieving explainable artificial intelligence (XAI) by integrating deep learning with symbolic reasoning. The core claim is to translate the predictions of deep learning models into interpretable symbolic rules and then leverage these rules for symbolic reasoning. This framework utilizes knowledge graphs and logic programming to convert the internal representations of deep learning models into symbolic knowledge. The mechanism involves a process of mapping learned features from deep neural networks into a formal knowledge representation, followed by applying logical inference rules to derive new knowledge or explain existing predictions. The novelty lies in the deep fusion of these two approaches, offering a pathway to increased transparency and trustworthiness in AI systems. The proposed framework addresses the limitations of black-box deep learning models by providing a transparent, rule-based explanation of their decisions. This work contributes to the growing field of XAI by offering a concrete methodology for bridging the gap between the statistical power of deep learning and the interpretability of symbolic reasoning.