  Efforts in the recommendation community are shifting from the sole emphasis
on utility to considering beyond-utility factors, such as fairness and
robustness. Robustness of recommendation models is typically linked to their
ability to maintain the original utility when subjected to attacks. Limited
research has explored the robustness of a recommendation model in terms of
fairness, e.g., the parity in performance across groups, under attack
scenarios. In this paper, we aim to assess the robustness of graph-based
recommender systems concerning fairness, when exposed to attacks based on
edge-level perturbations. To this end, we considered four different fairness
operationalizations, including both consumer and provider perspectives.
Experiments on three datasets shed light on the impact of perturbations on the
targeted fairness notion, uncovering key shortcomings in existing evaluation
protocols for robustness. As an example, we observed perturbations affect
consumer fairness on a higher extent than provider fairness, with alarming
unfairness for the former. Source code:
https://github.com/jackmedda/CPFairRobust
