Path-Specific Counterfactual Fairness for Recommender Systems
Path-Specific Counterfactual Fairness for Recommender Systems
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DOI：
10.1145/3580305.3599462
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Yaochen Zhu;Jing Ma;Liang Wu;Qilnli Guo;Liang Hong;Jundong Li
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作者：
Yaochen Zhu;Jing Ma;Liang Wu;Qilnli Guo;Liang Hong;Jundong Li
推荐系统（RS）已经成为在线平台不可或缺的一部分。随着人们对算法公平性的日益关注，RS不仅需要提供高质量的个性化内容，还需要不根据用户的人口统计信息歧视用户。然而，现有的RS可能会捕获敏感特征和观察到的用户行为之间的不良相关性，导致有偏见的建议。大多数公平的RS通过完全阻止敏感特征对推荐的影响来解决这个问题。但是由于敏感特征也可能以公平的方式影响用户兴趣（例如，基于文化偏好的竞争），不加区别地消除所有敏感特征的影响，不可避免地降低推荐质量和必要的难度。为了解决这个问题，我们提出了一个路径特定的公平RS（PSF-RS）的建议。具体来说，我们总结了敏感功能和观察到的评级之间的所有公平和不公平的相关性到两个潜在的代理调解人，路径特定的偏见（PS偏差）的概念定义的基础上，路径特定的反事实推理。受Pearl的最小变化原则的启发，我们通过将有偏见的事实世界最小化转化为假设公平的世界来解决PS偏差，其中可以通过解决约束优化问题来相应地学习公平的RS模型。对于技术部分，我们提出了一种可行的PSF-RS实现，即，PSF-VAE，具有弱监督变分推理，其鲁棒地推断潜在的中介者，使得可以减轻不公平性，同时可以最大限度地保留必要的推荐差异。在半模拟和真实数据集上进行的实验证明了PSF-RS的有效性。
Recommender systems (RSs) have become an indispensable part of online platforms. With the growing concerns of algorithmic fairness, RSs are not only expected to deliver high-quality personalized content, but are also demanded not to discriminate against users based on their demographic information. However, existing RSs could capture undesirable correlations between sensitive features and observed user behaviors, leading to biased recommendations. Most fair RSs tackle this problem by completely blocking the influences of sensitive features on recommendations. But since sensitive features may also affect user interests in a fair manner (e.g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities. To address this challenge, we propose a path-specific fair RS (PSF-RS) for recommendations. Specifically, we summarize all fair and unfair correlations between sensitive features and observed ratings into two latent proxy mediators, where the concept of path-specific bias (PS-Bias) is defined based on path-specific counterfactual inference. Inspired by Pearl's minimal change principle, we address the PS-Bias by minimally transforming the biased factual world into a hypothetically fair world, where a fair RS model can be learned accordingly by solving a constrained optimization problem. For the technical part, we propose a feasible implementation of PSF-RS, i.e., PSF-VAE, with weakly-supervised variational inference, which robustly infers the latent mediators such that unfairness can be mitigated while necessary recommendation diversities can be maximally preserved simultaneously. Experiments conducted on semi-simulated and real-world datasets demonstrate the effectiveness of PSF-RS.