III: Small: Realizing Fairness in Recommender Systems: Intersectionality, Tools, Explanation

推荐系统为电子商务、社交媒体和许多其他类型的应用程序的用户提供个性化的建议。随着它们变得越来越普遍，推荐系统已经从消费者品味领域转移到具有更大社会影响和敏感性的领域，如金融服务、就业和住房。人们越来越担心，个性化推荐可能会表现出偏见，产生不公平的结果，并加剧不平等问题。这限制了推荐系统在就业等环境中的潜在效用，在这些环境中，公平对待用户是法律规定的。缺乏对公平的关注也意味着推荐系统倾向于强化偏见，并限制用户接触不同的项目。尽管这个问题对公众和研究人员最近的工作很重要，但在公平意识推荐的关键方面几乎没有进展。因此，那些网站严重依赖个性化推荐的公司很少能从研究团体那里得到关于如何应用公平意识推荐以及如何评估他们的努力相对于技术水平的指导。与此同时，由于缺乏既定的数据集和指标，推荐系统的研究人员很难在该领域取得进展。该项目将在公平意识推荐方面取得进展，使其适用于实际应用程序。为了满足这些需求，该项目将开发能够实现高精度的推荐模型和算法，同时在多个交叉维度上保持公平性，并探索它们在三个公平关键领域的有效性：慈善事业、就业和新闻。除了少数例外，现有的公平感知推荐算法都是在考虑单个公平维度（定义单个受保护组）的环境中开发和评估的。研究小组将扩展这些算法，使其对多个受保护特征敏感，并将推荐交易的多个方面纳入其中。众所周知，解释支持用户使用推荐系统，从而产生更大的信任。然而，具有公平性意识的推荐的更大复杂性使得很难做出解释，并且引入公平性目标实际上可能会降低对某些用户的信任，这些用户可能会认为系统对他们的利益反应不足。因此，该项目将开发公平建议的解释机制，以支持公平标准应用的透明度。最后，为了将公平感知推荐研究建立在更坚实的基础上，该项目将开发用于生成可用于开发和评估推荐算法的合成数据集的技术。该项目将使用潜在因素方法来表示用户-项目关联模式，包括与不同类型用户的关联，然后对这些因素进行抽样，以生成包含现实评级模式的合成数据。整个项目开发的软件将被纳入开源平台，以造福其他研究人员。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recommender systems provide personalized suggestions to users of e-commerce, social media, and many other types of applications. As they have become more prevalent, recommender systems have moved from areas of consumer taste to areas with greater social impact and sensitivity, such as financial services, employment, and housing. Concern has grown that personalized recommendations may exhibit bias, produce unfair results, and entrench problems of inequity. This limits the potential utility of recommender systems in environments such as employment, where fair treatment of users is legally mandated. Lack of attention to fairness has also meant that recommender systems have tended to reinforce biases and to limit users' exposure to diverse items. In spite of the importance of this issue to the public and the recent work of researchers, there is little progress on key aspects of fairness-aware recommendation. Companies whose sites depend heavily on personalized recommendation therefore have little guidance from the research community about how to apply fairness-aware recommendation and how to evaluate their efforts relative to the state of the art. At the same time, recommender systems researchers have difficulty making progress in the field because of the lack of established datasets and metrics. This project will make advances in fairness-aware recommendation that make it suitable for real-world applications. To meet these needs, the project will develop recommendation models and algorithms that can achieve high accuracy, while preserving fairness in multiple inter-sectional dimensions, and explore their effectiveness in three fairness-critical domains: philanthropy, employment, and news. Existing fairness-aware recommendation algorithms have, with few exceptions, been developed and evaluated in contexts where a single dimension of fairness, defining a single protected group, is considered. The research team will extend these algorithms to be sensitive to multiple protected features, and to incorporate multiple sides of the recommendation transaction. It is well known that explanations support users in their use of recommender systems, engendering greater trust. However, the greater complexity of fairness-aware recommendation makes it difficult to produce explanations, and the introduction of fairness objectives may actually decrease trust in some users who may perceive the system as insufficiently responsive to their interests. The project will therefore develop explanation mechanisms for fairness-aware recommendation that support transparency in the application of fairness criteria. Finally, in order to put fairness-aware recommendation research on a firmer foundation, this project will develop techniques for generating synthetic datasets that can be used in developing and evaluating recommendation algorithms. The project will use latent factor methods to represent patterns of user-item associations, including associations with users of different types, and then apply sampling to these factors to generate synthetic data containing realistic rating patterns. The software developed throughout the project will be incorporated into open-source platforms for the benefit of other researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.