论文标题
暴露的人的感知偏见：重新访问第一印象数据集
Person Perception Biases Exposed: Revisiting the First Impressions Dataset
论文作者
论文摘要
这项工作重新审视了查尔恩的第一印象数据库，并通过众包进行了成对比较，以注释人格感知。我们首次分析了原始的成对注释，并揭示了与性别，种族，年龄和面对吸引力等感知属性相关的现有人的看法偏见。我们展示了人们的感知偏见如何影响主观任务的数据标记，该任务几乎没有得到计算机视觉和机器学习社区的关注。我们进一步表明，如果不考虑特殊处理，用于将成对注释转换为连续值的机制可能会放大偏见。这项研究的发现与仍在创建有关主观任务的新数据集并将其用于实际应用程序的计算机视觉社区有关，而忽略了这些感知偏见。
This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness. We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases.
