  Over the years, there has been growing interest in using Machine Learning
techniques for biomedical data processing. When tackling these tasks, one needs
to bear in mind that biomedical data depends on a variety of characteristics,
such as demographic aspects (age, gender, etc) or the acquisition technology,
which might be unrelated with the target of the analysis. In supervised tasks,
failing to match the ground truth targets with respect to such characteristics,
called confounders, may lead to very misleading estimates of the predictive
performance. Many strategies have been proposed to handle confounders, ranging
from data selection, to normalization techniques, up to the use of training
algorithm for learning with imbalanced data. However, all these solutions
require the confounders to be known a priori. To this aim, we introduce a novel
index that is able to measure the confounding effect of a data attribute in a
bias-agnostic way. This index can be used to quantitatively compare the
confounding effects of different variables and to inform correction methods
such as normalization procedures or ad-hoc-prepared learning algorithms. The
effectiveness of this index is validated on both simulated data and real-world
neuroimaging data.
