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.

利用面部表情识别 (FER) 作为领域，通过分析性别人口的代表性和刻板性两种偏见，研究了数据集中的人口偏见对机器学习模型的传播情况。结果显示，代表性偏见对模型的影响较弱，而刻板性偏见具有显著的影响，主要集中在有偏见的类别上，尽管它也会影响无偏见类别的预测。这些结果强调了需要区分不同类型偏见的偏见分析，这对于有效的偏见缓解策略的开发至关重要。