… explainability to gain insights into model (un)fairness. In this paper, we study the problem of
explainable fairness, … guides the design of fair recommender systems with a more informed …

… Rather, we propose a fairness-aware algorithm so as to provide fair explainable diversity
leading to potential items of interest for recommendations. Specifically, we formalize this as a 0–…

… review for the explainable recommendation research. We first highlight the position of
explainable recommendation in recommender system research by categorizing recommendation …

… That said, we find Freitas’ review to be very important for transparency, fairness and
explainability: first, he argues that model size alone is not sufficient to measure model interpretability…

… explainable recommendation, we introduce two fundamental concepts to motivate the
explainable recommendation … Fairnessaware explainable recommendation over knowledge …

… fairness, this work has not been focused on recommendation. Here, we consider user
perspectives of fairness-aware recommender … Designing theory-driven user-centric explainable AI. …

… explainability and fairness and develop explainable fairness models in the future so that
explainability and fairness … Other research topics include the relationship between fairness and …

… of a trustworthy recommender system such as explainability, robustness, privacy, and so on.
Section 7 provides a big picture of future directions of fairness-aware recommender systems …

… AI engine to address problems regarding bias, fairness, and … personalized and fairness-aware
recommendations across … where he leads the fairness, transparency, explainability, and …

… [17] develop a counterfactual explainable fairness model for recommendation to explain
which item aspects inluence item exposure fairness. They perform perturbations on item aspect …