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。

Explainable Fairness in Recommendation

The issue of fairness in recommendation is becoming increasingly essential as
Recommender Systems touch and influence more and more people in their daily
lives. In fairness-aware recommendation, most of the existing algorithmic
approaches mainly aim at solving a constrained optimization problem by imposing
a constraint on the level of fairness while optimizing the main recommendation
objective, e.g., CTR. While this alleviates the impact of unfair
recommendations, the expected return of an approach may significantly
compromise the recommendation accuracy due to the inherent trade-off between
fairness and utility. This motivates us to deal with these conflicting
objectives and explore the optimal trade-off between them in recommendation.
One conspicuous approach is to seek a Pareto efficient solution to guarantee
optimal compromises between utility and fairness. Moreover, considering the
needs of real-world e-commerce platforms, it would be more desirable if we can
generalize the whole Pareto Frontier, so that the decision-makers can specify
any preference of one objective over another based on their current business
needs. Therefore, in this work, we propose a fairness-aware recommendation
framework using multi-objective reinforcement learning, called MoFIR, which is
able to learn a single parametric representation for optimal recommendation
policies over the space of all possible preferences. Specially, we modify
traditional DDPG by introducing conditioned network into it, which conditions
the networks directly on these preferences and outputs Q-value-vectors.
Experiments on several real-world recommendation datasets verify the
superiority of our framework on both fairness metrics and recommendation
measures when compared with all other baselines. We also extract the
approximate Pareto Frontier on real-world datasets generated by MoFIR and
compare to state-of-the-art fairness methods.

本文提出了一种基于多目标强化学习的公平感知的推荐框架（MoFIR），能够以单参数表示形式学习所有可能偏好的最优推荐策略，并在多个真实推荐数据集上的实验中验证了其在公平指标和推荐度量方面的优越性。