Less can be more: representational vs. stereotypical gender bias in facial expression recognition

TL;DR

This study investigates how demographic biases, specifically representational and stereotypical gender biases, propagate from datasets into facial expression recognition models, revealing stereotypical bias's stronger influence and emphasizing the need for differentiated bias analysis.

Contribution

The paper provides a detailed analysis of how different types of gender bias affect FER models, highlighting the importance of distinguishing bias types for effective mitigation strategies.

Findings

Representational bias has a weaker impact on models.

Stereotypical bias significantly influences model predictions.

Bias type differentiation is crucial for developing mitigation strategies.

Abstract

Machine learning models can inherit biases from their training data, leading to discriminatory or inaccurate predictions. This is particularly concerning with the increasing use of large, unsupervised datasets for training foundational models. Traditionally, demographic biases within these datasets have not been well-understood, limiting our ability to understand how they propagate to the models themselves. To address this issue, this paper investigates the propagation of demographic biases from datasets into machine learning models. We focus on the gender demographic component, analyzing two types of bias: representational and stereotypical. For our analysis, we consider the domain of facial expression recognition (FER), a field known to exhibit biases in most popular datasets. We use Affectnet, one of the largest FER datasets, as our baseline for carefully designing and generating subsets that incorporate varying strengths of both representational and stereotypical bias. Subsequently, we train several models on these biased subsets, evaluating their performance on a common test set to assess the propagation of bias into the models' predictions. Our results show that representational bias has a weaker impact than expected. Models exhibit a good generalization ability even in the absence of one gender in the training dataset. Conversely, stereotypical bias has a significantly stronger impact, primarily concentrated on the biased class, although it can also influence predictions for unbiased classes. These results highlight the need for a bias analysis that differentiates between types of bias, which is crucial for the development of effective bias mitigation strategies.