The absence of suitable visualization tools makes the comprehensibility of the parameter space difficult, leaving ambiguities regarding explored domains, actual reaction performance, and anticipated outcomes. In the case of AI-guided RO, an additional level of complexity is added because the AI's data (e.g., predicted values, uncertainty of predictions, or feature importance of a prediction) is often hard to grasp by humans without any additional tools to explore it. This makes it hard for scientists to understand how the model arrived at a particular prediction , which is important for users to detect possible flaws and shortcomings of the AI model and calibrate user trust . On the other hand, understanding an AI prediction may also help scientists to learn from a model if they deem the decision to be reasonable, enhancing their scientific understanding .