Uncovering the Bias in Facial Expressions
This research addresses the critical problem of detecting and understanding bias in AI systems for developers and researchers, particularly in sensitive applications like medical image analysis.
This paper investigates bias in facial expression detection, specifically Action Unit classification, by training a neural network and analyzing its performance quantitatively via accuracy and qualitatively via heatmaps. The study successfully detected bias related to gender and skin color, although it could not definitively attribute lower performance solely to these factors.
Over the past decades the machine and deep learning community has celebrated great achievements in challenging tasks such as image classification. The deep architecture of artificial neural networks together with the plenitude of available data makes it possible to describe highly complex relations. Yet, it is still impossible to fully capture what the deep learning model has learned and to verify that it operates fairly and without creating bias, especially in critical tasks, for instance those arising in the medical field. One example for such a task is the detection of distinct facial expressions, called Action Units, in facial images. Considering this specific task, our research aims to provide transparency regarding bias, specifically in relation to gender and skin color. We train a neural network for Action Unit classification and analyze its performance quantitatively based on its accuracy and qualitatively based on heatmaps. A structured review of our results indicates that we are able to detect bias. Even though we cannot conclude from our results that lower classification performance emerged solely from gender and skin color bias, these biases must be addressed, which is why we end by giving suggestions on how the detected bias can be avoided.