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Nowadays, research into personalization has been focusing on explainabilityand fairness. Several approaches proposed in recent works are able to explainindividual recommendations in a post-hoc manner or by explanation paths.However, explainability techniques applied to unfairness in recommendation havebeen limited to finding user/item features mostly related to biasedrecommendations. In this paper, we devised a novel algorithm that leveragescounterfactuality methods to discover user unfairness explanations in the formof user-item interactions. In our counterfactual framework, interactions arerepresented as edges in a bipartite graph, with users and items as nodes. OurBipartite Graph Explainer perturbs the topological structure to find an alteredversion (counterfactual explanation) that minimizes the disparity in utilitybetween the protected and unprotected demographic groups. Experiments on fourreal-world graphs coming from various domains showed that our method cansystematically explain user unfairness on three state-of-the-art GNN-basedrecommendation models. Moreover, an empirical evaluation of the perturbednetwork uncovered relevant patterns that justify the nature of the unfairnessdiscovered by the generated explanations. The source code and the preprocesseddata sets are available at https://github.com/jackmedda/RS-BGExplainer.

GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning