  Algorithmic Bias can be due to bias in the training data or issues with the
algorithm itself. These algorithmic issues typically relate to problems with
model capacity and regularisation. This underestimation bias may arise because
the model has been optimised for good generalisation accuracy without any
explicit consideration of bias or fairness. In a sense, we should not be
surprised that a model might be biased when it hasn't been "asked" not to be.
In this paper, we consider including bias (underestimation) as an additional
criterion in model training. We present a multi-objective optimisation strategy
using Pareto Simulated Annealing that optimise for both balanced accuracy and
underestimation. We demonstrate the effectiveness of this strategy on one
synthetic and two real-world datasets.
