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Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender.
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Graphgan: Graph representation learning with generative adversarial nets. In AAAI
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Mitigating unwanted biases with adversarial learning. In AIES
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
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Avishek Bose and William Hamilton. 2019 · 2019
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Sub-graph contrast for scalable self-supervised graph representation learning. In IEEE ICDM
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Towards a Unified Framework for Fair and Stable Graph Representation Learning
Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik. 2021a · 2021
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Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations
Chirag Agarwal, Marinka Zitnik, and Himabindu Lakkaraju. 2021b · 2021
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Individual Fairness for Graph Neural Networks: A Ranking based Approach. In KDD
Yushun Dong, Jian Kang, Hanghang Tong, and Jundong Li. 2021 · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021 · 2021
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