Emotion-based Stereotypes in Image Analysis Services
Emotion-based Stereotypes in Image Analysis Services
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DOI：
10.1145/3386392.3399567
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
K. Kyriakou;S. Kleanthous;Jahna Otterbacher;G. A. Papadopoulos
中科院分区：
文献类型：
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作者：
K. Kyriakou;S. Kleanthous;Jahna Otterbacher;G. A. Papadopoulos
基于视觉的认知服务（CogS）在从实时安全和社交网络到智能手机应用的广泛应用中变得至关重要。许多服务专注于分析人物图像。当涉及到面部分析时，这些服务可能具有误导性，甚至不准确，引发了道德问题，例如放大社会刻板印象。我们分析了流行的图像标记CogS，从一个人的脸推断情绪，考虑他们是否永久化的种族和性别的刻板印象有关的情绪。通过比较CogS和人类生成的描述一组控制图像，我们强调了CogS的透明度和公平性的需要。特别是，我们记录的证据表明，CogS实际上可能比群众工作者更有可能使“愤怒的黑人”的刻板印象永久化，并经常将黑人种族个体归因于“敌意情绪”。
Vision-based cognitive services (CogS) have become crucial in a wide range of applications, from real-time security and social networks to smartphone applications. Many services focus on analyzing people images. When it comes to facial analysis, these services can be misleading or even inaccurate, raising ethical concerns such as the amplification of social stereotypes. We analyzed popular Image Tagging CogS that infer emotion from a person's face, considering whether they perpetuate racial and gender stereotypes concerning emotion. By comparing both CogS and Human-generated descriptions on a set of controlled images, we highlight the need for transparency and fairness in CogS. In particular, we document evidence that CogS may actually be more likely than crowdworkers to perpetuate the stereotype of the "angry black man" and often attribute black race individuals with "emotions of hostility".