  It is fair to say that many of the prominent examples of bias in Machine
Learning (ML) arise from bias that is there in the training data. In fact, some
would argue that supervised ML algorithms cannot be biased, they reflect the
data on which they are trained. In this paper we demonstrate how ML algorithms
can misrepresent the training data through underestimation. We show how
irreducible error, regularization and feature and class imbalance can
contribute to this underestimation. The paper concludes with a demonstration of
how the careful management of synthetic counterfactuals can ameliorate the
impact of this underestimation bias.
