Artificial Intelligence (AI) continues to be a field shrouded in both fascination and mystery. Despite its widespread adoption and impressive capabilities, we often remain in the dark about how exactly AI systems operate and achieve their results. This opacity presents challenges as AI becomes more ingrained in various aspects of our lives, from healthcare to finance. Recently, an intriguing development has emerged in the quest to understand AI’s ‘mind’. Researchers have adopted innovative strategies to peek inside complex AI systems, particularly those driven by neural networks, to demystify their decision-making processes. This initiative is akin to solving a puzzle where we observe the outcomes but lack a comprehensive grasp of the mechanisms that generate these outcomes. At the core of this exploration is the idea of ‘Explainable AI.’ By breaking down the processes within machine learning models, experts aim to provide a clearer picture of how inputs are transformed into outputs. This not only enhances trust in AI systems but also addresses ethical concerns such as bias and accountability. Furthermore, there’s an imperative for enhancing AI safety. A better understanding of AI’s workings can pave the way for safer deployment, particularly in critical fields such as autonomous vehicles and healthcare. This exploration bridges the gap between AI’s potential and its responsible use, serving as a compass for future innovations. In this context, our focus shifts from mere functionality to transparency, aiming to build AI systems that are not just efficient but also aligned with societal values. This endeavor is gaining traction, with researchers and industry stakeholders converging to create an AI ecosystem that is both beneficial and understandable. As we delve deeper into unraveling AI’s intricacies, it becomes apparent that the future of AI hinges not just on its capabilities but also on our ability to comprehend and guide its evolution responsibly.