Web: http://arxiv.org/abs/2110.00530

Jan. 24, 2022, 2:11 a.m. | Tai Le Quy, Arjun Roy, Vasileios Iosifidis, Wenbin Zhang, Eirini Ntoutsi

cs.LG updates on arXiv.org arxiv.org

As decision-making increasingly relies on Machine Learning (ML) and (big)
data, the issue of fairness in data-driven Artificial Intelligence (AI) systems
is receiving increasing attention from both research and industry. A large
variety of fairness-aware machine learning solutions have been proposed which
involve fairness-related interventions in the data, learning algorithms and/or
model outputs. However, a vital part of proposing new approaches is evaluating
them empirically on benchmark datasets that represent realistic and diverse
settings. Therefore, in this paper, we overview …

arxiv datasets fairness learning machine machine learning survey

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