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The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data is independent and identically distributed (IID).
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Tai Le Quy, Arjun Roy, Vasileios Iosifidis, Wenbin Zhang, and Eirini Ntoutsi · 2022
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Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, and Le Song · 2022
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Debiasing graph representations via metadata-orthogonal training
John Palowitch and Bryan Perozzi · 2020
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Investigating and mitigating degree-related biases in graph convoltuional networks
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