This paper explores the critical role of explainable artificial intelligence (XAI) in bridging the gap between the high performance of deep learning models and the need for human interpretability. It investigates methods that enhance transparency and trust by providing meaningful explanations of complex model decisions, thereby addressing challenges posed by the black-box nature of deep neural networks. The study highlights the importance of developing interpretable AI systems to foster user trust and facilitate the integration of AI into sensitive domains such as healthcare and finance. Ultimately, this research aims to advance the understanding and implementation of XAI to ensure responsible and effective AI deployment in the modern era.
Khan et al. (Wed,) studied this question.