Explainable Fuzzy Logic in AI: Enhancing Transparency and Trust in Deep Learning Models

Deep learning models have been successfully applied in many fields, but as they are inherently black-box functions, their interpretability and trustworthiness are very limited. Explainable AI (XAI) has been developed to overcome these problems of interpretability, bringing more transparency and understandability to AI models. Fuzzy Logic is one of the approaches that can bridge the gap between machine learning and human reasoning systems making it a very powerful tool which makes AI systems more interpretable. The focus of this paper is to combine fuzzy logic in deep learning and gain explainability without compromising predictive performance. We review several explainable fuzzy logic paradigms and discuss how they offer a unique solution to the model interpretability problem by creating a link between AI decision-making and human-readable rationale. Using fuzzy logic to enhance deep learning can provide improved performance (when designed correctly) with better understanding of how the model works compared to traditional deep learning models due to the transparent nature of the fuzzy logic system. We also discuss the applications of explainable fuzzy logic in sensitive areas like healthcare, finance, and autonomous systems where trust and transparency are critical. It also identifies the challenges to be addressed and future research directions in building fuzzy-enhanced explainable AI frameworks. Fuzzy logic-based approaches to decision-making can help AI systems deliver more interpretable and trustable outcomes, thus increasing their adoption in high-impact areas. The research outcomes help develop explainability within AI systems, thus leading to the deployment of AI in a more ethical and responsible manner.