Leveraging Semi-Supervised Learning for Fairness using Neural Networks
This addresses fairness concerns in decision-making systems, particularly in scenarios with limited labeled data, but appears incremental as it applies semi-supervised learning to a known bottleneck.
The paper tackles the problem of fairness in machine learning decision-making systems by proposing a semi-supervised algorithm using neural networks, called SSFair, which leverages unlabeled data to improve both performance and fairness, though no concrete numbers are provided.
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 systems. This inspired us to tackle the challenge of fairness by formulating the problem in a semi-supervised framework. In this paper, we propose a semi-supervised algorithm using neural networks benefiting from unlabeled data to not just improve the performance but also improve the fairness of the decision-making process. The proposed model, called SSFair, exploits the information in the unlabeled data to mitigate the bias in the training data.