Leveraging Semi-Supervised Learning for Fairness using Neural Networks
Leveraging Semi-Supervised Learning for Fairness using Neural Networks
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DOI：
10.1109/icmla.2019.00017
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
V. Noroozi;S. Bahaadini;Samira Sheikhi;Nooshin Mojab;Philip S. Yu
中科院分区：
文献类型：
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作者：
V. Noroozi;S. Bahaadini;Samira Sheikhi;Nooshin Mojab;Philip S. Yu
人们越来越关注基于机器学习的决策系统的公平性。标记数据的短缺一直是基于机器学习的系统面临的一个挑战性问题。在这种情况下，半监督学习已被证明是利用未标记数据来提高模型性能的有效方法。值得注意的是，未标记的数据不包含标签信息，而标签信息本身可能是训练机器学习系统时产生偏差的重要来源。这启发我们通过在半监督框架中制定问题来应对公平性的挑战。在本文中，我们提出了一个半监督算法，利用神经网络受益于未标记的数据，不仅提高了性能，而且还提高了决策过程的公平性。该模型称为SSFair，利用未标记数据中的信息来减轻训练数据中的偏差。
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.