Algorithmic fairness Explainability Recommender systems

GNNUERS: Unfairness Explanation in Recommender Systems through Counterfactually-Perturbed Graphs

Counterfactual reasoning can be effectively employed to perturb user-item interactions, to identify and explain unfairness in GNN-based recommender systems, thus paving the way for more equitable and transparent recommendations. In this study, in collaboration with Francesco Fabbri, Gianni Fenu, Mirko Marras, and Giacomo Medda, and published in the ACM Transactions on Intelligent Systems and Technology, …

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