Although face recognition has made impressive progress in recent years, we
ignore the racial bias of the recognition system when we pursue a high level of
accuracy. Previous work found that for different races, face recognition
networks focus on different facial regions, and the sensitive regions of
darker-skinned people are much smaller. Based on this discovery, we propose a
new de-bias method based on gradient attention, called Gradient Attention
Balance Network (GABN). Specifically, we use the gradient attention map (GAM)
of the face recognition network to track the sensitive facial regions and make
the GAMs of different races tend to be consistent through adversarial learning.
This method mitigates the bias by making the network focus on similar facial
regions. In addition, we also use masks to erase the Top-N sensitive facial
regions, forcing the network to allocate its attention to a larger facial
region. This method expands the sensitive region of darker-skinned people and
further reduces the gap between GAM of darker-skinned people and GAM of
Caucasians. Extensive experiments show that GABN successfully mitigates racial
bias in face recognition and learns more balanced performance for people of
different races.

提出了一种新的基于梯度注意力的去偏置方法，称为 Gradient Attention Balance Network (GABN)，通过将面部识别网络的梯度注意力图用于跟踪敏感的面部区域，并通过对抗性学习使不同种族的梯度注意力图趋于一致，该方法通过使网络专注于相似的面部区域来缓解偏见。同时，该方法还使用口罩来擦除前 N 个敏感面部区域，迫使网络将其注意力分配到更大的面部区域，扩大了皮肤较暗的人的敏感区域，进一步减少了皮肤较暗的人的梯度注意力图和高加索人的梯度注意力图之间的差距。广泛的实验证明，GABN 成功地减轻了面部识别中的种族偏见，并为不同种族的人学习到更平衡的表现。

梯度注意平衡网络：通过梯度注意力缓解人脸识别的种族偏见

Gradient Attention Balance Network: Mitigating Face Recognition Racial  Bias via Gradient Attention

Classification models learn to generalize the associations between data
samples and their target classes. However, researchers have increasingly
observed that machine learning practice easily leads to systematic errors in AI
applications, a phenomenon referred to as AI blindspots. Such blindspots arise
when a model is trained with training samples (e.g., cat/dog classification)
where important patterns (e.g., black cats) are missing or
periphery/undesirable patterns (e.g., dogs with grass background) are
misleading towards a certain class. Even more sophisticated techniques cannot
guarantee to capture, reason about, and prevent the spurious associations. In
this work, we propose ESCAPE, a visual analytic system that promotes a
human-in-the-loop workflow for countering systematic errors. By allowing human
users to easily inspect spurious associations, the system facilitates users to
spontaneously recognize concepts associated misclassifications and evaluate
mitigation strategies that can reduce biased associations. We also propose two
statistical approaches, relative concept association to better quantify the
associations between a concept and instances, and debias method to mitigate
spurious associations. We demonstrate the utility of our proposed ESCAPE system
and statistical measures through extensive evaluation including quantitative
experiments, usage scenarios, expert interviews, and controlled user
experiments.

该研究提出了一种视觉分析系统（ESCAPE），通过人类参与者的观测和量化，以及减轻偏见的解决方案（debias 方法），在避免人工智能盲点（AI blindspots）和系统性错误方面具有较高的实用性。