FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models-Reference-Cited by-同舟云学术

FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models

Published:2023-07-18 Issue: Volume: Page:
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Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Abdelrazek Mohamed¹^ORCID,Purificato Erasmo²^ORCID,Boratto Ludovico³^ORCID,De Luca Ernesto William²^ORCID

Affiliation:

1. Otto von Guericke University Magdeburg, Magdeburg, Germany

2. Otto von Guericke University Magdeburg & Leibniz Institute for Educational Media | Georg Eckert Institute, Magdeburg, Germany

3. University of Cagliari, Cagliari, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591814

Reference35 articles.

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

3. Dan Biddle . 2017. Adverse impact and test validation: A practitioner's guide to valid and defensible employment testing . Routledge . Dan Biddle. 2017. Adverse impact and test validation: A practitioner's guide to valid and defensible employment testing. Routledge.

4. 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).

5. Weijian Chen , Fuli Feng , Qifan Wang , Xiangnan He , Chonggang Song , Guohui Ling , and Yongdong Zhang . 2021. CatGCN: Graph Convolutional Networks with Categorical Node Features . IEEE Transactions on Knowledge and Data Engineering ( 2021 ). Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. CatGCN: Graph Convolutional Networks with Categorical Node Features. IEEE Transactions on Knowledge and Data Engineering (2021).

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