Understanding how AI models arrive at their conclusions is crucial. The concept of explainable AI (XaI) seeks to demystify the "black box" of neural networks, allowing us to grasp the reasoning behind predictions. While achieving specific explanations can be challenging, exploring the influences on outcomes offers valuable insights into AI decision-making processes.
