Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets

By Chris Dulhanty, Alexander Wong. 2019

47 citations recorded by the source corpus.

The ImageNet dataset ushered in a flood of academic and industry interest in deep learning for computer vision applications. Despite its significant impact, there has not been a comprehensive investigation into the demographic attributes of images contained within the dataset. Such a study could lead to new insights on inherent biases within ImageNet, particularly important given it is frequently used to pretrain models for a wide variety of computer vision tasks. In this work, we introduce a model-driven framework for the automatic annotation of apparent age and gender attributes in large-scale image datasets. Using this framework, we conduct the first demographic audit of the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) subset of ImageNet and the "person" hierarchical category of ImageNet. We find that 41.62% of faces in ILSVRC appear as female, 1.71% appear as individuals above the age of 60, and males aged 15 to 29 account for the largest subgroup with 27.11%. We note that the presented model-driven framework is not fair for all intersectional groups, so annotation are subject to bias. We present this work as the starting point for future development of unbiased annotation models and for the study of downstream effects of imbalances in the demographics of ImageNet. Code and annotations are available at: this http URL

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jia Deng
2. Olga Russakovsky
3. Li Fei-Fei
4. Abeba Birhane
5. Vinay Uday Prabhu
6. Kaiyu Yang
7. Kristen Grauman
8. Jacqueline Yau

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Li Fei-Fei
3. Xiaogang Wang
4. Andrew Zisserman
5. Trevor Darrell
6. Dacheng Tao
7. Yi Yang
8. Andrea Vedaldi

In AI Ethics, Fairness & Society

1. Abeba Birhane
2. Vinay Uday Prabhu
3. Olga Russakovsky

Citations in the corpus

• Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy (2019, neutral): “So far, Dulhanty and Wong [22] studied the demographics of people in ILSVRC data by using computer vision models to predict the gender and age of depicted people, and demonstrated that, e.”
• Large image datasets: A pyrrhic win for computer vision? (2020, methodology): “This audit also entailed a human-in-the-loop based hybrid-approach that the pre-trained-model annotations (along the lines of [27, 92]) to segment large dataset into smaller sub-sets and hand-label the smaller subsets to generate two lists covering 62 misogynistic images and 30 image-classes with…”
• A Study of Face Obfuscation in ImageNet (2021, background): “For ImageNet, researchers have examined and attempted to mitigate issues such as geographic diversity, the category vocabulary, and imbalanced representation (Shankar et al., 2017; Stock & Cisse, 2018; Dulhanty & Wong, 2019; Yang et al., 2020).”