Shap (Shapley additive explanations) values are used in Explainable AI to better understand the output of machine learning models. It helps interpret prediction models as it shows the contribution and importance of each attribute on the predictions. Synthetic data can be used to transparently share this information. To calculate Shap values, it is necessary to have access to the prediction model and data. Without access to the original data for privacy reasons, a synthetic copy of the original data can be created, and because it contains all the relationships present in the original data, it can allow anyone using the predictive model to interpret it.