Integrating Fuzzy Logic with Deep Learning: A New Approach to Explainable Artificial Intelligence

The rapid progress in artificial intelligence (AI) has led to the development of robust deep learning models, yet their black box nature presents major challenges regarding interpretability and transparency. This article presents a novel method for explainable artificial intelligence (XAI) that merges Fuzzy Logic with deep learning. Fuzzy logic is renowned for its capability to manage uncertainty and facilitate approximate reasoning, providing a clear framework that enhances the decision-making capabilities of deep learning models. By integrating systems grounded in fuzzy rules, our goal is to boost the interpretability of deep learning models while maintaining their predictive performance. The proposed strategy connects human-understandable logic with the intricate calculations of neural networks, shedding light on the internal mechanisms of artificial intelligence systems. We validate the effectiveness of this combined approach through various case studies and experiments, showcasing enhanced transparency and reliability of the models. This fusion of Fuzzy Logic with deep learning adds to the expanding domain of XAI and holds promise for broader applications where explainability is crucial, such as in healthcare, finance, and autonomous systems.