Recently pre-trained multimodal models, such as CLIP, have shown exceptional
capabilities towards connecting images and natural language. The textual
representations in English can be desirably transferred to multilingualism and
support downstream multimodal tasks for different languages. Nevertheless, the
principle of multilingual fairness is rarely scrutinized: do multilingual
multimodal models treat languages equally? Are their performances biased
towards particular languages? To answer these questions, we view language as
the fairness recipient and introduce two new fairness notions, multilingual
individual fairness and multilingual group fairness, for pre-trained multimodal
models. Multilingual individual fairness requires that text snippets expressing
similar semantics in different languages connect similarly to images, while
multilingual group fairness requires equalized predictive performance across
languages. We characterize the extent to which pre-trained multilingual
vision-and-language representations are individually fair across languages.
However, extensive experiments demonstrate that multilingual representations do
not satisfy group fairness: (1) there is a severe multilingual accuracy
disparity issue; (2) the errors exhibit biases across languages conditioning
the group of people in the images, including race, gender and age.

本文通过引入多语言个体公平和多语言群体公平这两个公平性概念，分析了预训练的多模态模型是否公平地对待多种语言，结果表明预训练模型在各种语言上的表现差异很大，缺乏多语言群体公平特性，并且对于涉及种族、性别和年龄等方面的人物群体，模型存在一定的偏误。

评估预训练多模态表示中的多语言公平性

Assessing Multilingual Fairness in Pre-trained Multimodal  Representations

With the widespread deployment of large-scale prediction systems in
high-stakes domains, e.g., face recognition, criminal justice, etc., disparity
in prediction accuracy between different demographic subgroups has called for
fundamental understanding on the source of such disparity and algorithmic
intervention to mitigate it. In this paper, we study the accuracy disparity
problem in regression. To begin with, we first propose an error decomposition
theorem, which decomposes the accuracy disparity into the distance between
marginal label distributions and the distance between conditional
representations, to help explain why such accuracy disparity appears in
practice. Motivated by this error decomposition and the general idea of
distribution alignment with statistical distances, we then propose an algorithm
to reduce this disparity, and analyze its game-theoretic optima of the proposed
objective functions. To corroborate our theoretical findings, we also conduct
experiments on five benchmark datasets. The experimental results suggest that
our proposed algorithms can effectively mitigate accuracy disparity while
maintaining the predictive power of the regression models.

本研究探讨回归预测的准确度不平等问题，提出误差分解定理将准确度不平等划分为边缘标签分布和条件表示之间的距离，并通过统计距离来解决不平等问题，最终在 5 个基准数据集上进行实验，结果表明我们的算法能够有效缓解准确度不平等问题并保持回归模型的预测能力。

回归问题中精度失衡的理解和缓解

Understanding and Mitigating Accuracy Disparity in Regression

Adversarial training algorithms have been proved to be reliable to improve
machine learning models' robustness against adversarial examples. However, we
find that adversarial training algorithms tend to introduce severe disparity of
accuracy and robustness between different groups of data. For instance, a PGD
adversarially trained ResNet18 model on CIFAR-10 has 93% clean accuracy and 67%
PGD l-infty-8 robust accuracy on the class "automobile" but only 65% and 17% on
the class "cat". This phenomenon happens in balanced datasets and does not
exist in naturally trained models when only using clean samples. In this work,
we empirically and theoretically show that this phenomenon can happen under
general adversarial training algorithms which minimize DNN models' robust
errors. Motivated by these findings, we propose a Fair-Robust-Learning (FRL)
framework to mitigate this unfairness problem when doing adversarial defenses.
Experimental results validate the effectiveness of FRL.

研究发现，对于一些平衡的数据集，在执行 Adversarial training algorithms 时将出现不同类别的数据准确度和鲁棒性的严重差异，因此提出了一种名为 Fair-Robust-Learning 的框架以解决这种不公平的问题，并在实验中验证了其有效性。