Gender Artifacts in Visual Datasets

TL;DR

This paper investigates the prevalence of gender artifacts in large-scale visual datasets like COCO and OpenImages, revealing their widespread presence and challenging the feasibility of removing gender cues to mitigate biases.

Contribution

The study systematically analyzes gender artifacts in visual datasets, demonstrating their ubiquity and arguing that removing them is largely infeasible, emphasizing the need for robust methods.

Findings

Gender artifacts are pervasive across various levels of image information.

Removing gender artifacts from datasets is largely infeasible.

Researchers should develop models robust to gender distribution shifts.

Abstract

Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigating gender biases, often by attempting to remove gender expression information from images. To understand the feasibility and practicality of these approaches, we investigate what $\textit{gender artifacts}$ exist within large-scale visual datasets. We define a $\textit{gender artifact}$ as a visual cue that is correlated with gender, focusing specifically on those cues that are learnable by a modern image classifier and have an interpretable human corollary. Through our analyses, we find that gender artifacts are ubiquitous in the COCO and OpenImages datasets, occurring everywhere from low-level information (e.g., the mean value of the color channels) to the higher-level composition of the image (e.g., pose and location of people). Given the prevalence of gender artifacts, we claim that attempts to remove gender artifacts from such datasets are largely infeasible. Instead, the responsibility lies with researchers and practitioners to be aware that the distribution of images within datasets is highly gendered and hence develop methods which are robust to these distributional shifts across groups.