Understand which parameters were most important for an AI model's prediction, and whether this information overlaps with an expert's judgment. Detect and overcome possible flaws and shortcomings of AI models to make informed decisions for the next batch of experiments. Learn from AI reasoning and enhance experts' knowledge about reaction parameters. (C4) Data uncertainty: The RO campaign may start with no prior measurement data or direct knowledge of the reaction to be optimized. Only after the first cycle of experiments (randomly chosen or chosen by experts) do we have a small set of measurement data that is used to model the whole parameter space. Estimations of experiment settings that are close to an experiment that was already performed can be made with higher confidence than experiments that lie in an area that is not explored at all. The optimization process is usually a trade-off between exploring uncertain areas of the parameter space and exploiting slight variations of experiment settings that performed well in previous experiments. Therefore, it is important to illustrate the estimation of a value together with the (un)certainty of the estimation.