  In recommendation literature, explainability and fairness are becoming two
prominent perspectives to consider. However, prior works have mostly addressed
them separately, for instance by explaining to consumers why a certain item was
recommended or mitigating disparate impacts in recommendation utility. None of
them has leveraged explainability techniques to inform unfairness mitigation.
In this paper, we propose an approach that relies on counterfactual
explanations to augment the set of user-item interactions, such that using them
while inferring recommendations leads to fairer outcomes. Modeling user-item
interactions as a bipartite graph, our approach augments the latter by
identifying new user-item edges that not only can explain the original
unfairness by design, but can also mitigate it. Experiments on two public data
sets show that our approach effectively leads to a better trade-off between
fairness and recommendation utility compared with state-of-the-art mitigation
procedures. We further analyze the characteristics of added edges to highlight
key unfairness patterns. Source code available at
https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023.
