  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.
