Abstract:
Fairness is a topic in machine learning con-
cerned with removing biases present in
machine learning systems. One method
for removing such biases is the reduc-
tions approach by Agarwal et al.1, which
has the notable advantage of being usable
for a wide range of fairness de nitions.
Past work shows that this method gives a
good fairness-accuracy tradeo compared
to other methods that enforce fairness.
In this paper, the performance of this
method will be further investigated. To
do this, I will compare the method against
a preprocessing method for the demo-
graphic parity fairness defi nition.
