XAI enhances transparency by breaking down complex machine learning models into understandable components. For instance, in reaction mechanisms, XAI can identify which variables (e.g., temperature, pressure, concentration) most significantly affect the reaction rate. This level of detail allows researchers to understand not just what decisions the AI is making, but why it is making them.
