FAI: Fairness-Aware Algorithms for Network Analysis

FAI：用于网络分析的公平感知算法

• 批准号: 1939368
• 负责人: Pang-Ning Tan
• 金额: $ 35.98万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939368&HistoricalAwards=false
• 关键词: FAI; Fairness; Aware; Algorithms; Network

As society becomes increasingly reliant on artificial intelligence (AI) technology, there have been growing concerns whether the decisions generated by the AI systems may lead to discriminatory actions against certain protected groups in the population. These concerns have brought increasing scrutiny into the issue of fairness in AI systems and their underlying machine learning algorithms. To overcome this challenge, the overarching goal of this project is to develop fairness-aware algorithms that can maintain the high utility of decisions generated by the AI systems without discriminating against particular subgroups of the population. Specifically, this research will address fundamental issues of fairness in algorithms that utilize network data in their decision making. Successful completion of this project will not only produce novel algorithms for researchers, but also tools that can help practitioners assess the level of inequality present in social networking platforms. The undergraduate and graduate students who participate in the project will be trained to conduct cutting edge research in AI and network science. The investigators will also seek collaborative partnership with research scientists from the industry to apply the developed methods in order to expand their social impact beyond the academic community.This research fills a major gap in current research on fairness in AI, which has primarily focused on independent and identically distributed (i.i.d.) data. There are still questions remain whether the existing methods are effective when applied to network data. In particular, the link structure of the network often contains information about the protected attributes (e.g., gender, race, or sexual orientation), and thus, must be taken into consideration in the design of fairness-aware machine learning algorithms. To address this issue, the objectives of this project are two-fold: (1) To develop metrics for assessing fairness in network learning algorithms and (2) To design, implement, and evaluate network learning algorithms that consider the tradeoff between fairness and utility of the models for various network analysis tasks and applications (including community detection and link prediction). The innovative methods developed in this project will be a step forward towards bridging the gap between current understanding of fairness in i.i.d. data and its application to network analysis.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.

随着社会越来越依赖人工智能（AI）技术，人们越来越担心人工智能系统产生的决定是否会导致对人口中某些受保护群体的歧视性行动。这些担忧使得人们越来越关注人工智能系统及其底层机器学习算法的公平性问题。为了克服这一挑战，该项目的总体目标是开发公平意识算法，该算法可以保持人工智能系统生成的决策的高实用性，而不会歧视特定的群体。具体来说，这项研究将解决在决策中利用网络数据的算法中的公平性的基本问题。该项目的成功完成不仅将为研究人员提供新的算法，还将为从业人员提供工具，帮助他们评估社交网络平台上存在的不平等程度。参与该项目的本科生和研究生将接受培训，以进行人工智能和网络科学的前沿研究。研究人员还将寻求与行业研究科学家的合作伙伴关系，应用开发的方法，以扩大其对学术界以外的社会影响。这项研究填补了当前人工智能公平性研究的一个重大空白，该研究主要集中在独立和同分布（i.i.d.）数据现有的方法在应用于网络数据时是否有效仍然存在问题。具体地，网络的链路结构通常包含关于受保护属性的信息（例如，性别、种族或性取向），因此，在设计公平感知机器学习算法时必须考虑到这些因素。为了解决这个问题，这个项目的目标有两个方面：（1）开发用于评估网络学习算法公平性的指标;（2）设计、实现和评估网络学习算法，这些算法考虑了各种网络分析任务和应用（包括社区检测和链接预测）的模型的公平性和实用性之间的权衡。本项目中开发的创新方法将是缩小目前对i.i.d.公平性的理解之间的差距的一步。该奖项反映了NSF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。

项目成果

Influence Propagation for Linear Threshold Model with Graph Neural Networks

• DOI: 10.1109/icdmw60847.2023.00149
• 发表时间: 2023-12
• 期刊: 2023 IEEE International Conference on Data Mining Workshops (ICDMW)
• 作者: Francisco Santos;Anna Stephens;Pang-Ning Tan;A. Esfahanian

Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

• DOI: 10.1609/aaai.v34i01.5429
• 发表时间: 2020-04
• 作者: Farzan Masrour;T. Wilson;Heng Yan;P. Tan;A. Esfahanian

Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups

• DOI: 10.1109/icdmw60847.2023.00050
• 发表时间: 2023-12
• 期刊: 2023 IEEE International Conference on Data Mining Workshops (ICDMW)
• 作者: Anna Stephens;Francisco Santos;Pang-Ning Tan;A. Esfahanian

Fairness-Aware Graph Sampling for Network Analysis

用于网络分析的公平感知图采样

• 发表时间: 2022
• 期刊: Proceedings of the IEEE International Conference on Data Mining
• 作者: Masrour, Farzan;Santos, Francisco;Tan, Pang-Ning;Esfahanian, Abdol-Hossein
• 通讯作者: Esfahanian, Abdol-Hossein

Fairness Perception from a Network-Centric Perspective

• DOI: 10.1109/icdm50108.2020.00145
• 发表时间: 2020-10
• 期刊: 2020 IEEE International Conference on Data Mining (ICDM)
• 作者: Farzan Masrour;P. Tan;A. Esfahanian