An Investigation Into Race Bias in Random Forest Models Based on Breast
  DCE-MRI Derived Radiomics Features
Mohamed Huti Tiarna Lee Elinor Sawyer Andrew P. King
Recent research has shown that artificial intelligence (AI) models can
exhibit bias in performance when trained using data that are imbalanced by
protected attribute(s). Most work to date has focused on deep learning models,
but classical AI techniques that make use of hand-crafted features may also be
susceptible to such bias. In this paper we investigate the potential for race
bias in random forest (RF) models trained using radiomics features. Our
application is prediction of tumour molecular subtype from dynamic contrast
enhanced magnetic resonance imaging (DCE-MRI) of breast cancer patients. Our
results show that radiomics features derived from DCE-MRI data do contain
race-identifiable information, and that RF models can be trained to predict
White and Black race from these data with 60-70% accuracy, depending on the
subset of features used. Furthermore, RF models trained to predict tumour
molecular subtype using race-imbalanced data seem to produce biased behaviour,
exhibiting better performance on test data from the race on which they were
trained.