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

III：媒介：协作研究：通过社会选择进行公平推荐

• 批准号: 2107505
• 负责人: Nicholas Mattei
• 金额: $ 24.98万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107505&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Fair

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.

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，并且是美国人互联网体验的核心。然而，人们越来越担心这些系统可能会传播偏见，将少数群体排除在推荐结果之外，并为具有非主流观点的个人提供不太理想的表现，从而对个人和社会产生更广泛的负面影响。这些问题以及其他潜在危害已成为最近研究关注的主题。然而，这项工作的实际成功是有限的，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性，并且因为它在很大程度上仍然脱离了现实世界的组织实践。在这项研究中，研究人员将克服这两个限制。他们将对非营利组织内的公平性进行详细的背景分析，确保他们的公平概念植根于实际的组织需求。公平推荐的后续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性作为一个情境复杂概念的理解，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。该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感知推荐算法，该算法本质上是多代理的，并且基于算法博弈论。从这个新颖的角度来看，该项目将重新将推荐公平性重新表述为社会选择分配和聚合问题的结合，将公平性关注和个性化推荐规定结合起来，并在此基础上衍生出新的推荐技术。研究人员将与非营利合作伙伴合作，对不同的利益相关者进行访谈和焦点小组讨论，建立在该组织背景下实施公平的不同方式的模型，并将这些技术推广到其他组织。该项目将创建其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 NSF 的法定使命，并被认为值得通过使用评估来支持基金会的智力价值和更广泛的影响审查标准。

项目成果

Dynamic Fairness-Aware Recommendation through Multi-Agent Social Choice

通过多智能体社会选择的动态公平感知推荐

• 发表时间: 2023-07
• 期刊: th International Workshop on Computational Social Choice (COMSOC 2023
• 作者: Aird, A.;Farastu, P.;Sun, J.;Voida, A.;Mattei, N.;Burke, R.
• 通讯作者: Burke, R.

The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation

公平的多面性：探索多利益相关方小额贷款建议的制度逻辑

• DOI: 10.1145/3593013.3594106
• 发表时间: 2023-06
• 期刊: and Transparency
• 作者: Smith, Jessie J.;Buhayh, Anas;Kathait, Anushka;Ragothaman, Pradeep;Mattei, Nicholas;Burke, Robin;Voida, Amy
• 通讯作者: Voida, Amy

Multi-agent Social Choice for Dynamic Fairness-aware Recommendation

动态公平感知推荐的多代理社会选择

• DOI: 10.1145/3511047.3538032
• 发表时间: 2022-07
• 期刊: Adaptation and Personalization (UMAP '22 Adjunct
• 作者: Burke, Robin;Mattei, Nicholas;Grozin, Vladislav;Voida, Amy;Sonboli, Nasim
• 通讯作者: Sonboli, Nasim

A Performance-preserving Fairness Intervention for Adaptive Microfinance Recommendation

针对适应性小额信贷建议的保绩效公平干预

• 发表时间: 2022-07
• 期刊: KDD Workshop on Online and Adaptive Recommender Systems at the 28th SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Burke, R.;Ragothaman, P.;Mattei, N.;Kimmig, B.;Voida, A.;Sonboli, N.;Kathait, A.;Fabros, M.
• 通讯作者: Fabros, M.

Algorithmic fairness, institutional logics, and social choice

算法公平、制度逻辑和社会选择

• 发表时间: 2022-07
• 期刊: Social Responsibility of Algorithms 2022
• 作者: Burke, R.;Voida, A.;Mattei, N.;Sonboli, N.;Eskandanian, F.
• 通讯作者: Eskandanian, F.