Counterfactual examples constitute a popular form of explanations that are most often generated through the optimisation of a cost function that combines different components of the explanation quality. This paper focuses on the final aggregation of the objective term, that depends on the considered machine learning task, and the subjective term, that depends on the targeted user and more precisely on their knowledge. It discusses the desired properties of this aggregation operator and proposes to use two forms of the Gödel integral operator, highlighting the expressiveness and appropriateness they offer.