Existing research on fairness-aware recommendation has mainly focused on the
quantification of fairness and the development of fair recommendation models,
neither of which studies a more substantial problem--identifying the underlying
reason of model disparity in recommendation. This information is critical for
recommender system designers to understand the intrinsic recommendation
mechanism and provides insights on how to improve model fairness to decision
makers. Fortunately, with the rapid development of Explainable AI, we can use
model explainability to gain insights into model (un)fairness. In this paper,
we study the problem of explainable fairness, which helps to gain insights
about why a system is fair or unfair, and guides the design of fair recommender
systems with a more informed and unified methodology. Particularly, we focus on
a common setting with feature-aware recommendation and exposure unfairness, but
the proposed explainable fairness framework is general and can be applied to
other recommendation settings and fairness definitions. We propose a
Counterfactual Explainable Fairness framework, called CEF, which generates
explanations about model fairness that can improve the fairness without
significantly hurting the performance.The CEF framework formulates an
optimization problem to learn the "minimal" change of the input features that
changes the recommendation results to a certain level of fairness. Based on the
counterfactual recommendation result of each feature, we calculate an
explainability score in terms of the fairness-utility trade-off to rank all the
feature-based explanations, and select the top ones as fairness explanations.

该研究基于可解释的 AI 技术，提出了一个计数事实可解释公平框架（CEF）来解决推荐系统中存在的公平性缺陷问题。该模型能够生成关于模型公平性的解释，以提供有关即使做出相关更改也不会显著损害其性能的公平性分数排名的信息。