FAI: Fairness-Aware Algorithms for Network Analysis

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。这些担忧促使人们对人工智能系统及其基础机器学习算法的公平性问题进行了越来越多的审查。为了克服这一挑战，该项目的首要目标是开发公平感知算法，该算法可以保持人工智能系统生成的决策的高实用性，而不歧视特定的人群子组。具体地说，这项研究将解决在决策过程中利用网络数据的算法中的基本公平问题。该项目的成功完成不仅将为研究人员提供新的算法，还将提供帮助从业者评估社交网络平台中存在的不平等程度的工具。参与该项目的本科生和研究生将接受培训，进行人工智能和网络科学方面的前沿研究。调查人员还将寻求与行业研究科学家的合作伙伴关系，以应用所开发的方法，以扩大其在学术界以外的社会影响。这项研究填补了目前人工智能公平研究的一个主要空白，该研究主要集中在独立和相同分布(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。具体地说，网络的链路结构通常包含关于受保护属性(例如，性别、种族或性取向)的信息，因此，在设计公平感知的机器学习算法时必须将其考虑在内。为了解决这个问题，这个项目的目标有两个：(1)开发评估网络学习算法中公平性的度量标准；(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)模型的公平性和实用性之间的权衡。该项目开发的创新方法将朝着弥合目前对国际移民组织公平性的理解之间的差距向前迈进一步。数据及其在网络分析中的应用。该奖项反映了NSF的法定使命，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。

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.