  Nowadays, research into personalization has been focusing on explainability
and fairness. Several approaches proposed in recent works are able to explain
individual recommendations in a post-hoc manner or by explanation paths.
However, explainability techniques applied to unfairness in recommendation have
been limited to finding user/item features mostly related to biased
recommendations. In this paper, we devised a novel algorithm that leverages
counterfactuality methods to discover user unfairness explanations in the form
of user-item interactions. In our counterfactual framework, interactions are
represented as edges in a bipartite graph, with users and items as nodes. Our
bipartite graph explainer perturbs the topological structure to find an altered
version that minimizes the disparity in utility between the protected and
unprotected demographic groups. Experiments on four real-world graphs coming
from various domains showed that our method can systematically explain user
unfairness on three state-of-the-art GNN-based recommendation models. Moreover,
an empirical evaluation of the perturbed network uncovered relevant patterns
that justify the nature of the unfairness discovered by the generated
explanations. The source code and the preprocessed data sets are available at
https://github.com/jackmedda/RS-BGExplainer.
