Lately, there have been several attempts to reduce bias in artificial
intelligence in order to maintain fairness in machine learning projects.
These methods fall under three categories of pre-processing, in processing
and post-processing techniques. There are at least 21 notation of fairness
in the recent literature, which not only provide different measurement
methods of fairness but also lead to completely different concepts. It is
worth mentioning that, it is impossible to satisfy all of the definitions
of fairness at the same time and some of them are incompatible with each
other. As a result, it is important to choose a fairness definition that
need to be satisfied according to the context that we are working on.
The current study investigates some of the most common definitions and
metrics for fairness introduced by researchers to compare three of the
proposed de-biasing techniques regarding their effects on the performance
and fairness measures through empirical experiments on four different
datasets. The de-biasing methods include the "Reweighting Algorithm",
"Adversarial De-biasing Method", the "Reject Option Classification Method"
performed on the classification tasks of "Survival of patients with heart
failure", "Prediction of hospital readmission among diabetes patients",
"Credit classification of bank account holders", and "The COVID19 related
anxiety level classification of Canadians".
Findings show the adversarial de-biasing in-processing method can be the
best technique for mitigating bias working with the deep learning
classifiers when we are capable of changing the classification process.
This method has not led to a considerable reduction of accuracy except for
the CAMH dataset.