Not Judging a User by Their Cover: Understanding Harm in Multi-Modal Processing within Social Media Research
Not Judging a User by Their Cover: Understanding Harm in Multi-Modal Processing within Social Media Research
复制标题
不要以封面来判断用户：了解社交媒体研究中多模式处理的危害
DOI：
10.1145/3422841.3423534
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Soroush Vosoughi
中科院分区：
文献类型：
--
作者：
Jiachen Jiang;Soroush Vosoughi
社交媒体已经动摇了我们社会的基础，尽管看起来不太可能。然而，许多用于缓和有害数字内容的流行工具受到学术界和公共领域的广泛批评，因为它们的性能中等，缺乏问责制。虽然社交媒体研究被认为主要集中在自然语言处理，我们证明了社区需要了解多媒体处理及其独特的道德考虑。具体来说，我们确定了在提供不同形式的信息时Amazon Turk（MTurk）注释器性能的统计差异，并讨论了众包人类人口统计预测所产生的伤害模式。最后，我们讨论了这些偏见的后果，通过审计的毒性检测器称为透视API的Twitter用户的语言在各种人口统计类别的性能。
Social media has shaken the foundations of our society, unlikely as it may seem. Many of the popular tools used to moderate harmful digital content, however, have received widespread criticism from both the academic community and the public sphere for middling performance and lack of accountability. Though social media research is thought to center primarily on natural language processing, we demonstrate the need for the community to understand multimedia processing and its unique ethical considerations. Specifically, we identify statistical differences in the performance of Amazon Turk (MTurk) annotators when different modalities of information are provided and discuss the patterns of harm that arise from crowd-sourced human demographic prediction. Finally, we discuss the consequences of those biases through auditing the performance of a toxicity detector called Perspective API on the language of Twitter users across a variety of demographic categories.