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Title: Individual Fairness in Graphs Using Local and Global Structural Information
Graph neural networks are powerful graph representation learners in which node representations are highly influenced by features of neighboring nodes. Prior work on individual fairness in graphs has focused only on node features rather than structural issues. However, from the perspective of fairness in high-stakes applications, structural fairness is also important, and the learned representations may be systematically and undesirably biased against unprivileged individuals due to a lack of structural awareness in the learning process. In this work, we propose a pre-processing bias mitigation approach for individual fairness that gives importance to local and global structural features. We mitigate the local structure discrepancy of the graph embedding via a locally fair PageRank method. We address the global structure disproportion between pairs of nodes by introducing truncated singular value decomposition-based pairwise node similarities. Empirically, the proposed pre-processed fair structural features have superior performance in individual fairness metrics compared to the state-of-the-art methods while maintaining prediction performance.  more » « less
Award ID(s):
2007941 2152117
PAR ID:
10566077
Author(s) / Creator(s):
; ;
Publisher / Repository:
The AAAI Press
Date Published:
Journal Name:
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
Volume:
7
ISSN:
3065-8365
Page Range / eLocation ID:
1379 to 1389
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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