CAREER: Foundations for Fair Social Network Analysis

An increasing amount of decision making is influenced by algorithms, and while this has resulted in clear benefits to society, the possible harms are starting to become apparent. In a striking example of such harms, recent works have argued that use of algorithms can perpetuate or create new forms of unethical discrimination. The area of social network analysis, in which individuals interact with one another through a complex set of connections, contains many worrisome applications.

For example, some credit scoring companies use social network data (e.g., friends and family connections) to assign credit scores to individuals; and in such applications, it is important to ensure that the algorithms used in such a process are not inadvertently discriminating against individuals on the basis of protected attributes like race or sex. In this project, the investigator will develop algorithms for the area of fair social network analysis.

The goal of fair social network analysis is to understand how network structure and network algorithms may lead to systematic harm against groups of individuals, and to propose remedies for such cases. This project is among the first in the area of fair social network analysis, and its contributions will be of value to both practitioners and researchers working with network data.

The resulting algorithms may be used in applications like online advertisement targeting and social media friendship recommendation. In addition to the scientific objectives of the project, the investigator will conduct activities related to course development in the area of ethical algorithm design, co-development of Continuing Legal Education seminars for attorneys (focusing on ethics of algorithms), outreach to local rural students, and development of a handbook on guidelines for technologists working with community organizations.

While there is a growing body of research on fairness in machine learning, existing methods do not consider dependencies between points, and so do not apply to network tasks like link prediction or community detection. The project will contain three tasks. In the first task, the investigator will design tests for determining whether unfairness exists in a network structure or network analysis.

In the second task, the investigator will design algorithms to reduce the unfairness in network analysis. In the third task, the investigator will design algorithms for modifying a network to reduce unfairness in its structure. The main contributions of this project will be (1) Development of formal definitions of fairness in networks, (2) Creation of algorithmic tests to detect unfairness in network structure, (3) Design of tests to determine reliance of a network analysis algorithm on a particular attribute, (4) Development of algorithms to mitigate such reliance, and (5) Development of algorithms to modify a network to reduce wrongful bias.

Education and outreach activities include course development in algorithmic design, and engagement with the legal community and community organizations.

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.