III: Medium: Collaborative Research: Evaluating and Maximizing Fairness in Information Flow on Networks

社交网络(你认识的人和你能联系到的人)有助于确定招聘机会、教育和健康信息的获取途径。他们根据网络地位对社会资本进行编码，在一个获取信息对进步至关重要的时代，这种社会资本可能非常有价值。在这个项目中，我们研究了对社交网络的干预，以缓解访问不平等，即从您所在的网络中出现的获取信息的差异。该项目将开发新的数学和计算模型，以表征社交网络中位置的差异如何放大访问不平等，以及改变网络结构的技术，既增加信息流动，又减少总体不平等。最后，该项目将开发实验方法来评估人类代理人在此类在线社交网络中的行为，以评估设计的干预措施的有效性。该项目将支持对人数不足的本科生和研究生群体进行指导和培训，并通过开放源码软件库和在该领域的顶级研究场所组织活动来传播这项工作。我们将开发数学和计算工具，用于分析网络信息访问的公平性。从那里，我们将表征缓解信息获取差距的算法难度，开发有效的估计器来预测这些差距，并设计干预策略来缩小这些差距。我们还将考虑如何描述共享相似信息获取途径的人群的特征。更广泛地说，我们试图将关于影响力最大化的研究与最近关于算法公平的工作联系起来：研究自动化程序如何延续或加剧边缘化群体现有的结构性劣势。通过这些努力开发的算法和结果将使用理论模型、由PI维护的网络存储库、真实世界的社交网络数据集和志愿者参与者的实验相结合进行评估。该奖项反映了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。

Social networks (whom you know and whom you can reach) help determine access to hiring opportunities, education, and health information. They encode social capital based on network position, and in an era where access to information is crucial for advancement, this social capital can be immensely valuable. In this project, we study interventions on social networks that mitigate access inequality, that is, differences in access to information that emerge from where you are in the network. This project will develop novel mathematical and computational models to characterize how differences in position in a social network can amplify inequalities of access, and techniques to change the structure of the network that both increase the flow of information and reduce the overall inequities. Finally, the project will develop experimental methodology to assess the behavior of human agents in such online social networks to assess the validity of the designed interventions. The project will support the mentoring and training of underrepresented populations of undergraduate and graduate students, as well as the dissemination of the work through open-source software repositories and event organization at the top research venues in the field. We will develop mathematical and computational tools for the analysis of fairness in information access on networks. From there, we will characterize the algorithmic difficulty of mitigating information access gaps, develop efficient estimators to predict such gaps, and design intervention strategies to reduce these gaps. We will also consider how to characterize clusters of people who share similar access to information. More generally, we seek to connect the research on influence maximization to recent work on algorithmic fairness: the study of how automated procedures can perpetuate or exacerbate existing structural disadvantages of marginalized groups. The algorithms and results developed through these efforts will be evaluated using a combination of theoretical models, network repositories maintained by the PIs, real-world social network datasets, and experiments with volunteer participants.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.