Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users’ preferences from user-item networks. Despite emerging regulations addressing fairness of automated systems, unfairness issues in graph collaborative filtering remain underexplored, especially from the consumer’s perspective. While the recommendation literature has seen numerous contributions in the form of mitigation algorithms and comprehensive evaluation studies on consumer unfairness, only a few of these works have delved into GNNs. A notable gap exists in the formalization of the latest mitigation algorithms, as well as in their effectiveness and reliability on cutting-edge models. In this paper, we conduct an extensive analysis of one of the latest mitigation methods tailored for consumer fairness in GNN-based recommendation. The reproduced technique adjusts the system fairness level by learning a fair graph augmentation. Our study serves as a solid response to recent research highlighting unfairness issues in graph collaborative filtering. Under an experimental setup based on 11 GNNs, 5 non-GNN models, and 5 real-world networks across diverse domains, our investigation reveals that fair graph augmentation is consistently effective on high-utility models and large datasets. Experiments on the transferability of the fair augmented graph open new issues for future recommendation~studies.