Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation

TL;DR

This paper introduces a method using attribute manipulation to evaluate and diagnose biases related to gender and race in computer vision models, revealing skewed representations and potential sources of bias.

Contribution

It presents a novel approach employing image synthesis for counterfactual fairness assessment in black-box vision models, linking biases to societal stereotypes.

Findings

Commercial classifiers are influenced by gender and racial cues.

Skewed gender representations are found in online profession searches.

Biases may originate from societal stereotypes reflected in training data.

Abstract

Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups. Diagnosing and understanding the underlying true causes of model biases, however, are challenging tasks because modern computer vision systems rely on complex black-box models whose behaviors are hard to decode. We propose to use an encoder-decoder network developed for image attribute manipulation to synthesize facial images varying in the dimensions of gender and race while keeping other signals intact. We use these synthesized images to measure counterfactual fairness of commercial computer vision classifiers by examining the degree to which these classifiers are affected by gender and racial cues controlled in the images, e.g., feminine faces may elicit higher scores for the concept of nurse and lower scores for STEM-related concepts. We also report the skewed gender representations in an online search service on profession-related keywords, which may explain the origin of the biases encoded in the models.