III: Medium: Collaborative Research: Fair Recommendation Through Social Choice

推荐系统是机器学习系统，提供对信息、媒体和电子商务目录的个性化访问。这些系统被广泛使用，对美国人的互联网体验至关重要。然而，越来越多的人担心这些系统会对个人和社会产生负面影响，因为它们会传播偏见，将少数群体排除在推荐结果之外，并为持非主流观点的个人提供不太理想的表现。这些问题，以及其他潜在的危害，一直是最近研究关注的话题。然而，这项工作的实际成功受到了限制，因为公平通常是以简单、狭隘的方式来构想的，例如，相对于单个群体的公平，而且因为它在很大程度上与现实世界的组织实践脱节。在本研究中，研究者将克服这两个限制。他们将对非营利组织内的公平进行详细的背景分析，确保他们的公平概念基于实际的组织需求。随后的公平推荐的实施将通过代表和平衡不同利益相关者的观点来反映实践的复杂性。这项工作将增强我们对算法公平作为一个定位和复杂概念的理解，以及对公平机器学习整个生命周期中出现的发展挑战的理解。这个项目的多学科团队包括推荐系统、计算社会选择和慈善信息学方面的专家。该团队将创建新的公平意识推荐算法，这些算法本质上是基于算法博弈论的多智能体。基于这一新颖的优势，该项目将把推荐公平性重新表述为社会选择分配和聚合问题的结合，将公平性问题和个性化推荐条款结合起来，并在此基础上衍生出新的推荐技术。研究人员将与他们的非营利合作伙伴合作，与不同的利益相关者进行访谈和焦点小组讨论，建立在这种组织背景下实现公平的不同方式的模型，并将这些技术推广到其他组织。该项目将创建一个多利益相关者公平解决方案的模型部署，并使用定量和定性技术从用户和内部利益相关者的角度对其进行评估。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 are machine learning systems that provide personalized access to information, media and e-commerce catalogs. These systems are widely used and are central to Americans' experience of the Internet. However, concern has grown that these systems can have negative impacts on both individuals and society more generally, by propagating biases, excluding minoritized sub-groups from recommendation results, and offering less optimal performance to individuals with non-mainstream viewpoints. These issues, as well as other potential harms, have been the topic of recent research attention. However, the practical success of this work has been limited because fairness has generally been conceived in simple, narrow ways, e.g. fairness relative to a single group, and because it has remained largely divorced from real-world organizational practices. In this research, the investigators will overcome both of these limitations. They will conduct a detailed contextual analysis of fairness within a non-profit organization, ensuring that their fairness concepts are grounded in real organizational needs. The ensuing implementation of fair recommendation will reflect the complexities of practice by representing and balancing the viewpoints of different stakeholders. The work will enhance our understanding of algorithmic fairness as a situated and complex concept and of the development challenges arising throughout the full life-cycle of fair machine learning. The multidisciplinary team on this project includes experts in recommender systems, computational social choice, and philanthropic informatics. The team will create new fairness-aware recommendation algorithms that are fundamentally multi-agent in nature and based on algorithmic game theory. From this novel vantage point, the project will reformulate recommendation fairness as a combination of social choice allocation and aggregation problems, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. Working with their non-profit partner, the researchers will conduct interviews and focus groups with diverse stakeholders, building models of the different ways that fairness is operationalized within this organizational context, and generalize these techniques to apply to other organizations. The project will create a model deployment of their multi-stakeholder fairness solution and use both quantitative and qualitative techniques to evaluate it from the perspective of both users and internal stakeholders.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.