  In classification models fairness can be ensured by solving a constrained
optimization problem. We focus on fairness constraints like Disparate Impact,
Demographic Parity, and Equalized Odds, which are non-decomposable and
non-convex. Researchers define convex surrogates of the constraints and then
apply convex optimization frameworks to obtain fair classifiers. Surrogates
serve only as an upper bound to the actual constraints, and convexifying
fairness constraints might be challenging.
  We propose a neural network-based framework, \emph{FNNC}, to achieve fairness
while maintaining high accuracy in classification. The above fairness
constraints are included in the loss using Lagrangian multipliers. We prove
bounds on generalization errors for the constrained losses which asymptotically
go to zero. The network is optimized using two-step mini-batch stochastic
gradient descent. Our experiments show that FNNC performs as good as the state
of the art, if not better. The experimental evidence supplements our
theoretical guarantees. In summary, we have an automated solution to achieve
fairness in classification, which is easily extendable to many fairness
constraints.
