Learning visual representation of high quality is essential for image
classification. Recently, a series of contrastive representation learning
methods have achieved preeminent success. Particularly, SupCon outperformed the
dominant methods based on cross-entropy loss in representation learning.
However, we notice that there could be potential ethical risks in supervised
contrastive learning. In this paper, we for the first time analyze unfairness
caused by supervised contrastive learning and propose a new Fair Supervised
Contrastive Loss (FSCL) for fair visual representation learning. Inheriting the
philosophy of supervised contrastive learning, it encourages representation of
the same class to be closer to each other than that of different classes, while
ensuring fairness by penalizing the inclusion of sensitive attribute
information in representation. In addition, we introduce a group-wise
normalization to diminish the disparities of intra-group compactness and
inter-class separability between demographic groups that arouse unfair
classification. Through extensive experiments on CelebA and UTK Face, we
validate that the proposed method significantly outperforms SupCon and existing
state-of-the-art methods in terms of the trade-off between top-1 accuracy and
fairness. Moreover, our method is robust to the intensity of data bias and
effectively works in incomplete supervised settings. Our code is available at
this https URL

本文提出了一种公平的监督对比损失方法（Fair Supervised Contrastive Loss），旨在保证图像的视觉表达不带有敏感属性信息，同时通过组内归一化减少分组之间的差异，实现了在公平性和准确性上的平衡，并在 CelebA 和 UTK Face 的数据集上验证了该方法的有效性。

面部属性分类的公平对比学习

Fair Contrastive Learning for Facial Attribute Classification

Training and evaluation of fair classifiers is a challenging problem. This is
partly due to the fact that most fairness metrics of interest depend on both
the sensitive attribute information and label information of the data points.
In many scenarios it is not possible to collect large datasets with such
information. An alternate approach that is commonly used is to separately train
an attribute classifier on data with sensitive attribute information, and then
use it later in the ML pipeline to evaluate the bias of a given classifier.
While such decoupling helps alleviate the problem of demographic scarcity, it
raises several natural questions such as: how should the attribute classifier
be trained?, and how should one use a given attribute classifier for accurate
bias estimation? In this work we study this question from both theoretical and
empirical perspectives.
We first experimentally demonstrate that the test accuracy of the attribute
classifier is not always correlated with its effectiveness in bias estimation
for a downstream model. In order to further investigate this phenomenon, we
analyze an idealized theoretical model and characterize the structure of the
optimal classifier. Our analysis has surprising and counter-intuitive
implications where in certain regimes one might want to distribute the error of
the attribute classifier as unevenly as possible among the different subgroups.
Based on our analysis we develop heuristics for both training and using
attribute classifiers for bias estimation in the data scarce regime. We
empirically demonstrate the effectiveness of our approach on real and simulated
data.

研究公平分类器培训和评估的挑战，探讨对数据点的敏感属性信息和标签信息的公平性指标依赖性，以及在数据缺乏的情况下如何训练和使用属性分类器进行偏差估计。