Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images
Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images
复制标题
专有图像标记算法的公平性：对人物图像的跨平台审核
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Jahna Otterbacher
中科院分区：
文献类型：
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作者：
K. Kyriakou;Pinar Barlas;S. Kleanthous;Jahna Otterbacher
越来越多的人期望，算法的行为方式应该是社会公正的。我们考虑图像标记API的情况以及它们对人物图像的解释。图像标记器已经成为我们的信息生态系统中不可或缺的一部分，促进了新的视觉交流和共享模式。最近，它们作为认知服务被广泛使用。但是，虽然标记API为开发人员提供了一种廉价而方便的方式来为他们的创作添加功能，但大多数都是不透明的和专有的。通过对六个标记器的跨平台比较，我们发现它们的行为存在显著差异。虽然有些人在图像上提供了更多的解释，但他们可能会通过滥用与性别相关的标签和/或对一个人的外貌做出判断，对所描绘的人表现出不那么公平。我们还讨论了在算法系统不能以基本事实为基准的情况下研究公平性的困难。
There are increasing expectations that algorithms should behave in a manner that is socially just. We consider the case of image tagging APIs and their interpretations of people images. Image taggers have become indispensable in our information ecosystem, facilitating new modes of visual communication and sharing. Recently, they have become widely available as Cognitive Services. But while tagging APIs offer developers an inexpensive and convenient means to add functionality to their creations, most are opaque and proprietary. Through a cross-platform comparison of six taggers, we show that behaviors differ significantly. While some offer more interpretation on images, they may exhibit less fairness toward the depicted persons, by misuse of gender-related tags and/or making judgments on a person’s physical appearance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarked against a ground truth.
影响因子：
5.4
作者：
Ma, Debbie S.;Correll, Joshua;Wittenbrink, Bernd
通讯作者：
Wittenbrink, Bernd
所有人的隐私：确保公平公正的隐私保护
DOI：
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发表时间：
2018
期刊：
Accountability and Transparency
影响因子：
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作者：
Ekstrand, Michael D.;Joshaghani, Rezvan;Mehrpouyan, Hoda
通讯作者：
Mehrpouyan, Hoda