In this paper, we present an empirical study on image recognition fairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We experimentally demonstrate that classes are not equal and the fairness issue is prevalent for image classification models across various datasets, network architectures, and model capacities. Moreover, several intriguing properties of fairness are identified. First, the unfairness lies in problematic representation rather than classifier bias. Second, with the proposed concept of Model Prediction Bias, we investigate the origins of problematic representation during optimization. Our findings reveal that models tend to exhibit greater prediction biases for classes that are more challenging to recognize. It means that more other classes will be confused with harder classes. Then the False Positives (FPs) will dominate the learning in optimization, thus leading to their poor accuracy. Further, we conclude that data augmentation and representation learning algorithms improve overall performance by promoting fairness to some degree in image classification.

翻译：本文对图像识别中的公平性进行了实证研究，即像ImageNet这样均衡数据集中存在的极端类别准确率差异。我们通过实验证明，类别并非平等，且公平性问题普遍存在于跨不同数据集、网络架构和模型能力的图像分类模型中。此外，我们识别出公平性的若干有趣特性。第一，不公平性源于有问题的表征而非分类器偏差。第二，借助提出的模型预测偏差概念，我们探究了优化过程中有问题的表征的成因。研究发现，模型对更难识别的类别往往表现出更大的预测偏差。这意味着更多其他类别会与较难的类别混淆。随后，假阳性样本将在优化过程中主导学习，从而导致这些类别的准确率低下。进一步地，我们总结出数据增强和表征学习算法通过在一定程度上提升分类中的公平性来改善整体性能。