A Neural Network Framework for Fair ClassifierMachine learning models are extensively being used in decision making,
especially for prediction tasks. These models could be biased or unfair towards
a specific sensitive group either of a specific race, gender or age.
Researchers have put efforts into characterizing a particular definition of
fairness and enforcing them into the models. In this work, mainly we are
concerned with the following three definitions, Disparate Impact, Demographic
Parity and Equalized Odds. Researchers have shown that Equalized Odds cannot be
satisfied in calibrated classifiers unless the classifier is perfect. Hence the
primary challenge is to ensure a degree of fairness while guaranteeing as much
accuracy as possible.
Fairness constraints are complex and need not be convex. Incorporating them
into a machine learning algorithm is a significant challenge. Hence, many
researchers have tried to come up with a surrogate loss which is convex in
order to build fair classifiers. Besides, certain papers try to build fair
representations by preprocessing the data, irrespective of the classifier used.
Such methods, not only require a lot of unrealistic assumptions but also
require human engineered analytical solutions to build a machine learning
model. We instead propose an automated solution which is generalizable over any
fairness constraint. We use a neural network which is trained on batches and
directly enforces the fairness constraint as the loss function without
modifying it further. We have also experimented with other complex performance
measures such as H-mean loss, Q-mean-loss, F-measure; without the need for any
surrogate loss functions. Our experiments prove that the network achieves
similar performance as state of the art.
Thus, one can just plug-in appropriate loss function as per required fairness
constraint and performance measure of the classifier and train a neural network
to achieve that. poster.pdf visual_comparison.pdf