In this paper, we study Multiple Criteria Decision Aiding (MCDA) problems modeled using an additive value function. We consider an epistemic framework in which the preferences of the decision-maker are imprecisely specified, yielding a robust additive preference model. In this context, we are interested in explaining recommendations derived from such robust model using a transitive sequence of preference swaps. Previous work laid the foundations for explaining the necessary preference relation through a sequence of necessary preference swaps. We extend this to take into account non-necessary preference, yielding to so-called “questionable explanations”: a chain of alternatives which is non-increasing w.r.t preference of the decision-maker. This approach provides additional descriptive power for explaining robust recommendations. We propose an efficient resolution engine based on Mixed-Integer Linear Programs, and we conduct numerical experiments to assess the benefit of our explanation strategy.