Automated computer vision systems have been applied in many domains including
security, law enforcement, and personal devices, but recent reports suggest
that these systems may produce biased results, discriminating against people in
certain demographic groups. Diagnosing and understanding the underlying true
causes of model biases, however, are challenging tasks because modern computer
vision systems rely on complex black-box models whose behaviors are hard to
decode. We propose to use an encoder-decoder network developed for image
attribute manipulation to synthesize facial images varying in the dimensions of
gender and race while keeping other signals intact. We use these synthesized
images to measure counterfactual fairness of commercial computer vision
classifiers by examining the degree to which these classifiers are affected by
gender and racial cues controlled in the images, e.g., feminine faces may
elicit higher scores for the concept of nurse and lower scores for STEM-related
concepts. We also report the skewed gender representations in an online search
service on profession-related keywords, which may explain the origin of the
biases encoded in the models.

本文提出了一种使用编码器 - 解码器网络开发的人脸图像合成方法，以测量商业计算机视觉分类器的反事实公平性，并报告了在线搜索服务中与职业相关的关键字中出现的偏向性，用以解释模型偏差的来源。