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Leveraging Semi-Supervised Learning for Fairness using Neural Networks
[article]
2019
arXiv
pre-print
There has been a growing concern about the fairness of decision-making systems based on machine learning. The shortage of labeled data has been always a challenging problem facing machine learning based systems. In such scenarios, semi-supervised learning has shown to be an effective way of exploiting unlabeled data to improve upon the performance of model. Notably, unlabeled data do not contain label information which itself can be a significant source of bias in training machine learning
arXiv:1912.13230v1
fatcat:rxlkti454zcotbzclerbc27hsa