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建议。随着推荐系统变得越来越普遍，推荐系统已经从消费者喜好的领域转移到社会影响和敏感性更大的领域，如金融服务、就业和住房。越来越多的人担心，个性化的推荐可能会显示出偏见，产生不公平的结果，并巩固不公平的问题。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。缺乏对公平性的关注也意味着推荐系统往往会加剧偏见，限制用户对不同项目的接触。尽管这一问题对公众很重要，研究人员最近的工作也很重要，但在公平意识推荐的关键方面进展甚微。因此，那些网站严重依赖个性化推荐的公司，在如何应用公平意识的推荐以及如何相对于最先进的水平评估自己的努力方面，几乎没有来自研究界的指导。与此同时，由于缺乏既定的数据集和度量标准，推荐系统的研究人员在该领域难以取得进展。该项目将在公平感知推荐方面取得进展，使其适用于现实世界的应用。为了满足这些需求，该项目将开发能够实现高精确度的推荐模型和算法，同时在多个跨部门维度保持公平，并探索其在慈善、就业和新闻三个公平关键领域的有效性。除了极少数例外，现有的公平感知推荐算法都是在考虑定义单个保护组的单一公平维度的情况下开发和评估的。研究团队将扩展这些算法，使其对多个受保护的功能敏感，并纳入推荐交易的多个方面。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，公平意识推荐的更大复杂性使得解释变得困难，而公平目标的引入实际上可能会降低对一些用户的信任，这些用户可能会认为该系统对他们的利益反应不足。因此，该项目将制定公平意识建议的解释机制，以支持公平标准的应用的透明度。最后，为了将公平感知推荐研究建立在更坚实的基础上，该项目将开发生成合成数据集的技术，这些合成数据集可以用于开发和评估推荐算法。该项目将使用潜在因素方法来表示用户-项目关联的模式，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。在整个项目中开发的软件将被整合到开源平台中，以造福于其他研究人员。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.