abstract

Fairness in Graph Machine Learning: Recent Advances and Future Prospectives

Authors:

Oyku Deniz Kose,

Jundong LiAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 5794 - 5795

https://doi.org/10.1145/3580305.3599555

Published: 04 August 2023 Publication History

Abstract

Graph machine learning algorithms have become popular tools in helping us gain a deeper understanding of the ubiquitous graph data. Despite their effectiveness, most graph machine learning algorithms lack considerations for fairness, which can result in discriminatory outcomes against certain demographic subgroups or individuals. As a result, there is a growing societal concern about mitigating the bias exhibited in these algorithms. To tackle the problem of algorithmic bias in graph machine learning algorithms, this tutorial aims to provide a comprehensive overview of recent research progress in measuring and mitigating the bias in machine learning algorithms on graphs. Specifically, this tutorial first introduces several widely-used fairness notions and the corresponding metrics. Then, we present a well-organized review of the theoretical understanding of bias in graph machine learning algorithms, followed by a summary of existing techniques to debias graph machine learning algorithms. Furthermore, we demonstrate how different real-world applications benefit from these graph machine learning algorithms after debiasing. Finally, we provide insights on current research challenges and open questions to encourage further advances.

References

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Yushun Dong, Ninghao Liu, Brian Jalaian, and Jundong Li. 2022a. Edits: Modeling and mitigating data bias for graph neural networks. In WWW.

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Yushun Dong, Jing Ma, Chen Chen, and Jundong Li. 2022b. Fairness in Graph Mining: A Survey. arXiv preprint arXiv:2204.09888 (2022).

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Cited By

Dong YLei ZZheng ZWang SMa JHuang AChen CLi JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)PyGDebias: A Python Library for Debiasing in Graph LearningCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651239(1019-1022)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651239

Index Terms

Fairness in Graph Machine Learning: Recent Advances and Future Prospectives
1. Computing methodologies
  1. Machine learning

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Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 04 August 2023

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KDD '23

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Dong YLei ZZheng ZWang SMa JHuang AChen CLi JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)PyGDebias: A Python Library for Debiasing in Graph LearningCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651239(1019-1022)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651239

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