  Often, what is termed algorithmic bias in machine learning will be due to
historic bias in the training data. But sometimes the bias may be introduced
(or at least exacerbated) by the algorithm itself. The ways in which algorithms
can actually accentuate bias has not received a lot of attention with
researchers focusing directly on methods to eliminate bias - no matter the
source. In this paper we report on initial research to understand the factors
that contribute to bias in classification algorithms. We believe this is
important because underestimation bias is inextricably tied to regularization,
i.e. measures to address overfitting can accentuate bias.
