Fast & Fair: Efficient Second-Order Robust Optimization for Fairness in
  Machine Learning
Allen Minch Hung Anh Vu Anne Marie Warren
This project explores adversarial training techniques to develop fairer Deep
Neural Networks (DNNs) to mitigate the inherent bias they are known to exhibit.
DNNs are susceptible to inheriting bias with respect to sensitive attributes
such as race and gender, which can lead to life-altering outcomes (e.g.,
demographic bias in facial recognition software used to arrest a suspect). We
propose a robust optimization problem, which we demonstrate can improve
fairness in several datasets, both synthetic and real-world, using an affine
linear model. Leveraging second order information, we are able to find a
solution to our optimization problem more efficiently than a purely first order
method.