Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling-Reference-Cited by-同舟云学术

Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Purificato Erasmo¹,Boratto Ludovico²,De Luca Ernesto William¹

Affiliation:

1. Otto-von-Guericke-Universität Magdeburg & Leibniz-Institut für Bildungsmedien | Georg-Eckert-Institut, Magdeburg, Germany

2. University of Cagliari, Cagliari, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557584

Reference38 articles.

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2. Big data's disparate impact;Barocas Solon;Calif. L. Rev.,2016

3. Fairness in Criminal Justice Risk Assessments: The State of the Art

4. Alex Beutel , Jilin Chen , Zhe Zhao , and Ed H Chi . 2017 . Data de cisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017). Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017).

5. Simon Caton and Christian Haas . 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053 ( 2020 ). Simon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053 (2020).

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