Title of the talk: Algorithmic Fairness in Social Network Analysis: FairSNA
Speaker: Prof. Akrati Saxena, Leiden University
Day, Date, and Time: Wednesday, 28th February 2024, 3:00 PM to 4:00 PM
Venue: Seminar room IE 211, Second Floor, IEOR Building.
Abstract: In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, understanding structural bias and inequalities in social networks and designing fairness-aware methods for various research problems in social network analysis (SNA) have not received much attention. In this talk, I will highlight how the structural bias and inequalities of social networks impacts the fairness of different SNA methods using a case study of link prediction. I will first discuss link prediction methods and then the impact of structural inequalities on the fairness of link prediction. Next, I will discuss approaches to encounter structural biases for fair and diverse link prediction. I will cover one method in-depth for diverse link prediction using NodeSim network embedding method that efficiently captures the diverse neighbourhood while keeping more similar nodes closer in the context of a given node. This will provide insights on how fairness can be incorporated in SNA.
Finally, I will briefly introduce other projects going on in my research group (including analysing biases in communication patterns, modelling of affirmative actions, fake news mitigation, and online privacy lost) and my overall research interest.
Bio: Prof. Akrati Saxena is an assistant professor at the computer science and AI department of the Faculty of Science at Leiden University. Before joining Leiden University, she worked as a Research Fellow at NUS, Singapore and Technical University of Eindhoven, Netherlands. She has worked extensively on Network Science and Data Science.Her research interests include Social Network Analysis, Complex Networks, Computational Social Science, Data Science, and Fairness. She is interested in designing machine learning and deep learning based methods for complex network data. Her current research is focussed on understanding inequalities in complex networks and algorithmic fairness in network and data science.