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

翻译：推荐社区的研究重点正从单纯关注效用转向考虑超效用因素，例如公平性和鲁棒性。推荐模型的鲁棒性通常指其在遭受攻击时保持原始效用的能力。目前仅有少数研究探讨了推荐模型在攻击场景下公平性方面的鲁棒性，例如不同用户群体间性能的均衡性。本文旨在评估基于图的推荐系统在面临边级扰动攻击时，其公平性维度的鲁棒性。为此，我们考虑了四种不同的公平性操作化定义，涵盖消费者和提供者两种视角。在三个数据集上的实验揭示了扰动对目标公平性指标的深层影响，揭露了现有鲁棒性评估协议的关键缺陷。例如，我们发现扰动对消费者公平性的影响程度远高于提供者公平性，前者出现了令人警惕的不公平现象。源代码：https://github.com/jackmedda/CPFairRobust