  Explainable Recommendation has attracted a lot of attention due to a renewed
interest in explainable artificial intelligence. In particular, post-hoc
approaches have proved to be the most easily applicable ones to increasingly
complex recommendation models, which are then treated as black-boxes. The most
recent literature has shown that for post-hoc explanations based on local
surrogate models, there are problems related to the robustness of the approach
itself. This consideration becomes even more relevant in human-related tasks
like recommendation. The explanation also has the arduous task of enhancing
increasingly relevant aspects of user experience such as transparency or
trustworthiness. This paper aims to show how the characteristics of a classical
post-hoc model based on surrogates is strongly model-dependent and does not
prove to be accountable for the explanations generated.
