Fairness without Demographics through Adversarially Reweighted Learning-猫眼课题宝

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发表时间：

2020-06

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ArXiv

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其他

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Preethi Lahoti;Alex Beutel;Jilin Chen;Kang Lee;Flavien Prost;Nithum Thain;Xuezhi Wang;Ed H. Chi

以前的许多机器学习(ML)公平文献都假设数据集中存在种族和性别等受保护的特征，并依赖它们来缓解公平问题。然而，在实践中，隐私和监管等因素往往阻止收集受保护的特征，或将其用于训练或推理，严重限制了传统公平研究的适用性。因此，我们问：在我们甚至不知道受保护组成员身份的情况下，如何训练ML模型来提高公平性？在这项工作中，我们通过提出对抗性重加权学习(ARL)来解决这个问题。特别是，我们假设非保护特征和任务标签对于识别公平性问题是有价值的，并且可以用于共同训练对抗性重新加权方法以提高公平性。我们的结果表明，ARL改进了Rawlsian Max-Min公平性，在多个数据集中的最差情况保护组的AUC显著改善，表现优于最先进的替代方案。

Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns. However, in practice factors like privacy and regulation often preclude the collection of protected features, or their use for training or inference, severely limiting the applicability of traditional fairness research. Therefore we ask: How can we train a ML model to improve fairness when we do not even know the protected group memberships? In this work we address this problem by proposing Adversarially Reweighted Learning (ARL). In particular, we hypothesize that non-protected features and task labels are valuable for identifying fairness issues, and can be used to co-train an adversarial reweighting approach for improving fairness. Our results show that ARL improves Rawlsian Max-Min fairness, with significant AUC improvements for worst-case protected groups in multiple datasets,outperforming state-of-the-art alternatives.

Fairness without Demographics through Adversarially Reweighted Learning

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