This work addresses how to validate group fairness in image recognition
software. We propose a distribution-aware fairness testing approach (called
DistroFair) that systematically exposes class-level fairness violations in
image classifiers via a synergistic combination of out-of-distribution (OOD)
testing and semantic-preserving image mutation. DistroFair automatically learns
the distribution (e.g., number/orientation) of objects in a set of images. Then
it systematically mutates objects in the images to become OOD using three
semantic-preserving image mutations -- object deletion, object insertion and
object rotation. We evaluate DistroFair using two well-known datasets
(CityScapes and MS-COCO) and three major, commercial image recognition software
(namely, Amazon Rekognition, Google Cloud Vision and Azure Computer Vision).
Results show that about 21% of images generated by DistroFair reveal
class-level fairness violations using either ground truth or metamorphic
oracles. DistroFair is up to 2.3x more effective than two main baselines, i.e.,
(a) an approach which focuses on generating images only within the distribution
(ID) and (b) fairness analysis using only the original image dataset. We
further observed that DistroFair is efficient, it generates 460 images per
hour, on average. Finally, we evaluate the semantic validity of our approach
via a user study with 81 participants, using 30 real images and 30
corresponding mutated images generated by DistroFair. We found that images
generated by DistroFair are 80% as realistic as real-world images.

该研究提出了一种称为 DistroFair 的分布感知公平性测试方法，通过组合分布感知图像变异、无分布测试和语义保留图像变异等方法，系统地揭示了图像分类器中的类级公平性违规，并通过评估结果表明，DistroFair 比传统方法更加有效。

基于分布感知的公平性测试生成

Distribution-aware Fairness Test Generation

Despite the clear performance benefits of data augmentations, little is known
about why they are so effective. In this paper, we disentangle several key
mechanisms through which data augmentations operate. Establishing an exchange
rate between augmented and additional real data, we find that in
out-of-distribution testing scenarios, augmentations which yield samples that
are diverse, but inconsistent with the data distribution can be even more
valuable than additional training data. Moreover, we find that data
augmentations which encourage invariances can be more valuable than invariance
alone, especially on small and medium sized training sets. Following this
observation, we show that augmentations induce additional stochasticity during
training, effectively flattening the loss landscape.

本文旨在研究数据扩展的机制，通过评估增强数据与额外真实数据之间的交换率发现，多样而不一致的增强数据可以比额外的训练数据更有价值，在小和中型训练集上，鼓励不变性的数据扩展可以比不变性本身更有价值，并证明了增强数据在训练过程中引入额外的随机性，从而有效地平稳了损失函数。

数据增强价值的探究：关于规模定律、不变性和隐式正则化的调查

How Much Data Are Augmentations Worth? An Investigation into Scaling  Laws, Invariance, and Implicit Regularization

Out-of-distribution (OOD) testing is increasingly popular for evaluating a
machine learning system's ability to generalize beyond the biases of a training
set. OOD benchmarks are designed to present a different joint distribution of
data and labels between training and test time. VQA-CP has become the standard
OOD benchmark for visual question answering, but we discovered three troubling
practices in its current use. First, most published methods rely on explicit
knowledge of the construction of the OOD splits. They often rely on
``inverting'' the distribution of labels, e.g. answering mostly 'yes' when the
common training answer is 'no'. Second, the OOD test set is used for model
selection. Third, a model's in-domain performance is assessed after retraining
it on in-domain splits (VQA v2) that exhibit a more balanced distribution of
labels. These three practices defeat the objective of evaluating
generalization, and put into question the value of methods specifically
designed for this dataset. We show that embarrassingly-simple methods,
including one that generates answers at random, surpass the state of the art on
some question types. We provide short- and long-term solutions to avoid these
pitfalls and realize the benefits of OOD evaluation.

这篇研究论文探讨了视觉问答的 OOD 基准测试（VQA-CP）目前存在的问题，指出了现有方法的局限性以及可能带来的负面影响，并提出了一些简单的解决方案，以更准确地评估模型的泛化能力。