论文标题
性别斜率：通过属性操纵的计算机视觉模型的反事实公平性
Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation
论文作者
论文摘要
自动化的计算机视觉系统已应用于许多领域，包括安全性，执法和个人设备，但最近的报告表明，这些系统可能会产生偏见的结果，从而歧视某些人口统计组中的人。然而，诊断和理解模型偏见的基本真实原因是具有挑战性的任务，因为现代计算机视觉系统依赖于很难解码的复杂的黑盒模型。我们建议使用开发的编码器网络进行图像属性操纵，以合成在性别和种族方面变化的面部图像，同时保持其他信号完整。我们使用这些合成的图像来测量商用计算机视觉分类器的反事实公平性，来检查这些分类器受图像中控制的性别和种族提示影响的程度，例如，女性面孔可能会为护士的概念和较低的STEM相关概念带来更高的分数。我们还在与专业相关的关键字的在线搜索服务中报告了偏斜的性别表示，这可以解释模型中编码的偏见的起源。
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.
