  With the proliferation of algorithmic decision-making, increased scrutiny has
been placed on these systems. This paper explores the relationship between the
quality of the training data and the overall fairness of the models trained
with such data in the context of supervised classification. We measure key
fairness metrics across a range of algorithms over multiple image
classification datasets that have a varying level of noise in both the labels
and the training data itself. We describe noise in the labels as inaccuracies
in the labelling of the data in the training set and noise in the data as
distortions in the data, also in the training set. By adding noise to the
original datasets, we can explore the relationship between the quality of the
training data and the fairness of the output of the models trained on that
data.
