  Demographic biases in source datasets have been shown as one of the causes of
unfairness and discrimination in the predictions of Machine Learning models.
One of the most prominent types of demographic bias are statistical imbalances
in the representation of demographic groups in the datasets. In this paper, we
study the measurement of these biases by reviewing the existing metrics,
including those that can be borrowed from other disciplines. We develop a
taxonomy for the classification of these metrics, providing a practical guide
for the selection of appropriate metrics. To illustrate the utility of our
framework, and to further understand the practical characteristics of the
metrics, we conduct a case study of 20 datasets used in Facial Emotion
Recognition (FER), analyzing the biases present in them. Our experimental
results show that many metrics are redundant and that a reduced subset of
metrics may be sufficient to measure the amount of demographic bias. The paper
provides valuable insights for researchers in AI and related fields to mitigate
dataset bias and improve the fairness and accuracy of AI models. The code is
available at https://github.com/irisdominguez/dataset_bias_metrics.
