Explainable AI: Interpreting, Visualizing, and Understanding Deep Learning Models


Artificial Intelligence (AI) and Deep Learning have made significant strides in recent years, solving complex tasks across various domains. However, as models grow in complexity, they become increasingly challenging to interpret and understand. This paper explores the critical topic of Explainable AI (XAI), focusing on methods and techniques for interpreting, visualizing, and understanding deep learning models. We delve into the significance of XAI in real-world applications, discuss state-of-the-art techniques, and explore the ethical and societal implications of AI opacity. Furthermore, we highlight the future directions of XAI research and its role in shaping responsible AI development.