Person Perception Biases Exposed: Revisiting the First Impressions Dataset

Julio C. S. Jacques Junior, Àgata Lapedriza, Cristina Palmero, Xavier Baró, Sergio Escalera
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引用次数: 5

Abstract

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.
人的感知偏见暴露:重新访问第一印象数据集
这项工作重新审视了ChaLearn第一印象数据库,通过众包进行两两比较,对个性感知进行了注释。我们首次分析了原始的两两注释,并揭示了与感知属性(如性别、种族、年龄和面部吸引力)相关的现有个人感知偏见。我们展示了人的感知偏差如何影响主观任务的数据标记,这一点目前很少受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差。本研究的发现与计算机视觉社区相关,该社区仍在创建关于主观任务的新数据集,并将其用于实际应用,忽略这些感知偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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