公平性GAN:使用生成式对抗网络生成具有公平性属性的数据集

IF 1.3 4区 计算机科学 Q1 Computer Science
P. Sattigeri;S. C. Hoffman;V. Chenthamarakshan;K. R. Varshney
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引用次数: 108

摘要

我们介绍了公平GAN(生成对抗性网络),这是一种生成数据集的方法,该数据集似乎与给定的多媒体数据集相似,但在决策中相对于受保护的属性更公平。我们提出了一种新的辅助分类器GAN,它致力于人口统计的均等或机会均等,并在几个数据集上显示了经验结果,包括CelebFaces Attributes(CelebA)数据集、Quick,Draw!数据集,以及足球运动员图像和他们被调用的违规行为的数据集。所提出的公式非常适合吸收未标记的数据;我们利用这一点用更大的CelebA数据集来扩充足球数据集。该方法倾向于改善人口均等和机会平等,同时生成合理的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fairness GAN: Generating datasets with fairness properties using a generative adversarial network
We introduce the Fairness GAN (generative adversarial network), an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in decision making. We propose a novel auxiliary classifier GAN that strives for demographic parity or equality of opportunity and show empirical results on several datasets, including the CelebFaces Attributes (CelebA) dataset, the Quick, Draw! dataset, and a dataset of soccer player images and the offenses for which they were called. The proposed formulation is well suited to absorbing unlabeled data; we leverage this to augment the soccer dataset with the much larger CelebA dataset. The methodology tends to improve demographic parity and equality of opportunity while generating plausible images.
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来源期刊
IBM Journal of Research and Development
IBM Journal of Research and Development 工程技术-计算机:硬件
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: The IBM Journal of Research and Development is a peer-reviewed technical journal, published bimonthly, which features the work of authors in the science, technology and engineering of information systems. Papers are written for the worldwide scientific research and development community and knowledgeable professionals. Submitted papers are welcome from the IBM technical community and from non-IBM authors on topics relevant to the scientific and technical content of the Journal.
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