A Comprehensive Survey of Explainable Artificial Intelligence (XAI) Methods: Exploring Transparency and Interpretability.

Artificial Intelligence (AI) is undergoing a significant transformation. In recent years, the deployment of AI models, from Analytical to Cognitive and Generative AI, has become imminent; however, the widespread utilization of these models has prompted questions and concerns within the research and business communities regarding their transparency and interpretability. A primary challenge lies in comprehending the underlying reasoning mechanisms employed by AI-enabled systems. The absence of transparency and interpretability into the decision-making process of these systems indicates a deficiency that can have severe consequences, e.g., in domains such as medical diagnosis and financial decision-making, where valuable resources are at stake. This survey explores Explainable AI (XAI) techniques within the AI system pipeline based on existing literature. It covers tools and applications across various domains, assessing current methods and addressing challenges and opportunities, particularly in the context of Generative AI.