Event
i-COMPASSE: Yanning Shen, Demystifying & Mitigating Unfairness for Machine Learning over Graphics
Tuesday, December 5, 2023
10:00 a.m.
1146 A.V. Williams and online on Zoom
Darcy Long
301 405 3114
dlong123@umd.edu
https://umd.zoom.us/j/97550658929
i-COMPASSE Colloquium
Demystifying and Mitigating Unfairness for Machine Learning over Graphics
Yanning Shen
Assistant Professor, EECS
University of California, Irvine
This hybrid event is in person in 1146 AV Williams, and on Zoom at
https://umd.zoom.us/j/97550658929
Meeting ID: 975 5065 8929
There is also a roundtable discussion at 2 p.m. in 2168 A.V. Williams.
Abstract: We live in an era of big data and "small world," where a large amount of data resides on inter-connected graphs representing a wide range of physical and social interdependencies, e.g., smart grids and social networks. Hence, machine learning (ML) over graphs has attracted significant attention and has shown promising success in various applications. Despite this success, the large-scale deployment of graph-based ML algorithms in real-world systems relies heavily on how socially responsible they are. While graph-based ML models nicely integrate the nodal data with the connectivity, they also inherit potential unfairness. Using such ML models may therefore result in inevitable unfair results in various decision- and policy-making in the related applications. To this end, this talk will introduce novel fairness-aware graph neural network (GNN) designs to address unfairness issues in learning over graphs. Furthermore, theoretical understandings are provided to explain the potential source of unfairness in GNNs and prove the efficacy of the proposed schemes. Experimental results on real networks are presented to demonstrate that the proposed framework can enhance fairness while providing comparable accuracy to state-of-the-art alternative approaches for node classification and link prediction tasks.
Bio: Yanning Shen is an assistant professor with the EECS department at the University of California, Irvine. Her research interests span the areas of machine learning, network science, and data science. She received her Ph.D. degree from the University of Minnesota in 2019. She was selected as a Rising Star in EECS by Stanford University in 2017. She received the Microsoft Academic Grant Award for AI Research in 2021, the Google Research Scholar Award in the area of Machine Learning and Data Mining in 2022, the Hellman Fellowship in 2022, and the UCI Newkirk faculty fellowship in 2023. She is also an honoree of the MIT Technology Review 35 Innovators under 35 Asia Pacific in 2022. More detailed information can be found at: https://sites.google.com/uci.edu/yanning-shen
