In this work, we present a framework to measure and mitigate intrinsic biases
with respect to protected variables --such as gender-- in visual recognition
tasks. We show that trained models significantly amplify the association of
target labels with gender beyond what one would expect from biased datasets.
Surprisingly, we show that even when datasets are balanced such that each label
co-occurs equally with each gender, learned models amplify the association
between labels and gender, as much as if data had not been balanced! To
mitigate this, we adopt an adversarial approach to remove unwanted features
corresponding to protected variables from intermediate representations in a
deep neural network -- and provide a detailed analysis of its effectiveness.
Experiments on two datasets: the COCO dataset (objects), and the imSitu dataset
(actions), show reductions in gender bias amplification while maintaining most
of the accuracy of the original models.

本文提出了一个框架来度量和降低视觉识别任务中与保护变量（例如性别）有关的固有偏差。通过采用对抗性方法来从深度神经网络中去除受保护变量的不想要的特征，我们展示了该方法的有效性，并在两个数据集上进行了实验。实验结果表明，在保持准确性的情况下，本方法减少了性别偏见的放大。