Evaluating the Reductions Intervention for Enforcing Fairness in Classi cation
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Issue Date
2019-06-28
Language
en
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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.
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Faculteit der Sociale Wetenschappen