Deep neural networks (DNNs) are often prone to learn the spurious
correlations between target classes and bias attributes, like gender and race,
inherent in a major portion of training data (bias-aligned samples), thus
showing unfair behavior and arising controversy in the modern pluralistic and
egalitarian society. In this paper, we propose a novel marginal debiased
network (MDN) to learn debiased representations. More specifically, a marginal
softmax loss (MSL) is designed by introducing the idea of margin penalty into
the fairness problem, which assigns a larger margin for bias-conflicting
samples (data without spurious correlations) than for bias-aligned ones, so as
to deemphasize the spurious correlations and improve generalization on unbiased
test criteria. To determine the margins, our MDN is optimized through a meta
learning framework. We propose a meta equalized loss (MEL) to perceive the
model fairness, and adaptively update the margin parameters by metaoptimization
which requires the trained model guided by the optimal margins should minimize
MEL computed on an unbiased meta-validation set. Extensive experiments on
BiasedMNIST, Corrupted CIFAR-10, CelebA and UTK-Face datasets demonstrate that
our MDN can achieve a remarkable performance on under-represented samples and
obtain superior debiased results against the previous approaches.

本文提出了一种新颖的边际无偏网络 (MDN) 来学习无偏表示，通过引入边际惩罚的思想设计了边际 softmax 损失 (MSL)，为解决公平性问题而优化 MDN，并通过元学习框架自适应更新边际参数，实现了在各种数据集上实验表明我们的 MDN 在少数人群样本上取得了显著的性能，并在对抗先前方法时获得更好的无偏结果。

边际无偏网络用于公平视觉识别

Marginal Debiased Network for Fair Visual Recognition

Machine learning systems produce biased results towards certain demographic
groups, known as the fairness problem. Recent approaches to tackle this problem
learn a latent code (i.e., representation) through disentangled representation
learning and then discard the latent code dimensions correlated with sensitive
attributes (e.g., gender). Nevertheless, these approaches may suffer from
incomplete disentanglement and overlook proxy attributes (proxies for sensitive
attributes) when processing real-world data, especially for unstructured data,
causing performance degradation in fairness and loss of useful information for
downstream tasks. In this paper, we propose a novel fairness framework that
performs debiasing with regard to both sensitive attributes and proxy
attributes, which boosts the prediction performance of downstream task models
without complete disentanglement. The main idea is to, first, leverage
gradient-based explanation to find two model focuses, 1) one focus for
predicting sensitive attributes and 2) the other focus for predicting
downstream task labels, and second, use them to perturb the latent code that
guides the training of downstream task models towards fairness and utility
goals. We show empirically that our framework works with both disentangled and
non-disentangled representation learning methods and achieves better
fairness-accuracy trade-off on unstructured and structured datasets than
previous state-of-the-art approaches.

本文提出了一种基于代理属性与敏感属性的公平性框架，通过梯度解释找到模型关注点，再利用它们来指导下游任务模型的训练，以实现公平性和效用之间的平衡。实验证明，我们的框架在非分离和分离表示学习方法上具有更好的公平性 - 精度平衡。