Abstract: 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: http://bit.ly/ImageNetDemoAudit

Introduction: ImageNet BIBREF0 , released in 2009, is a canonical dataset in computer vision. ImageNet follows the WordNet lexical database of English BIBREF1 , which groups words into synsets, each expressing a distinct concept. ImageNet contains 14,197,122 images in 21,841 synsets, collected through a comprehensive web-based search and annotated with Amazon Mechanical Turk (AMT) BIBREF0 . The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) BIBREF2 , held annually from 2010 to 2017, was the catalyst for an explosion of academic and industry interest in deep learning. A subset of 1,000 synsets were used in the ILSVRC classification task. Seminal work by Krizhevsky et al. BIBREF3 in the 2012 event cemented the deep convolutional neural network (CNN) as the preeminent model in computer vision. Today, work in computer vision largely follows a standard process: a pretrained CNN is downloaded with weights initialized to those trained on the 2012 ILSVRC subset of ImageNet, the network is adjusted to fit the desired task, and transfer learning is performed, where the CNN uses the pretrained weights as a starting point for training new data on the new task. The use of pretrained CNNs is instrumental in applications as varied as instance segmentation BIBREF4 and chest radiograph diagnosis BIBREF5 . By convention, computer vision practitioners have effectively abstracted away the details of ImageNet. While this has proved successful in practical applications, there is merit in taking a step back and scrutinizing common practices. In the ten years following the release of ImageNet, there has not been a comprehensive study into the composition of images in the classes it contains. This lack of scrutiny into ImageNet's contents is concerning. Without a conscious effort to incorporate diversity in data collection, undesirable biases can collect and propagate. These biases can manifest in the form of patterns learned from data that are influential in the decision of a model, but are not aligned with values of society BIBREF6 . Age, gender and racial biases have been exposed in word embeddings BIBREF7 , image captioning models BIBREF8 , and commercial computer vision gender classifiers BIBREF9 . In the case of ImageNet, there is some evidence that CNNs pretrained on its data may also encode undesirable biases. Using adversarial examples as a form of model criticism, Stock and Cisse BIBREF6 discovered that prototypical examples of the synset `basketball' contain images of black persons, despite a relative balance of race in the class. They hypothesized that an under-representation of black persons in other classes may lead to a biased representation of `basketball'. This paper is the first in a series of works to build a framework for the audit of the demographic attributes of ImageNet and other large image datasets. The main contributions of this work include the introduction of a model-driven demographic annotation pipeline for apparent age and gender, analysis of said annotation models and the presentation of annotations for each image in the training set of the ILSVRC 2012 subset of ImageNet (1.28M images) and the `person' hierarchical synset of ImageNet (1.18M images).

Question: How do they determine demographics on an image?
Answer: using model driven face detection, apparent age annotation and gender annotation