Biased Attention: Do Vision Transformers Amplify Gender Bias More than
  Convolutional Neural Networks?
Abhishek Mandal Susan Leavy Suzanne Little
Deep neural networks used in computer vision have been shown to exhibit many
social biases such as gender bias. Vision Transformers (ViTs) have become
increasingly popular in computer vision applications, outperforming
Convolutional Neural Networks (CNNs) in many tasks such as image
classification. However, given that research on mitigating bias in computer
vision has primarily focused on CNNs, it is important to evaluate the effect of
a different network architecture on the potential for bias amplification. In
this paper we therefore introduce a novel metric to measure bias in
architectures, Accuracy Difference. We examine bias amplification when models
belonging to these two architectures are used as a part of large multimodal
models, evaluating the different image encoders of Contrastive Language Image
Pretraining which is an important model used in many generative models such as
DALL-E and Stable Diffusion. Our experiments demonstrate that architecture can
play a role in amplifying social biases due to the different techniques
employed by the models for feature extraction and embedding as well as their
different learning properties. This research found that ViTs amplified gender
bias to a greater extent than CNNs