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This project explores adversarial training techniques to develop fairer DeepNeural Networks (DNNs) to mitigate the inherent bias they are known to exhibit.DNNs are susceptible to inheriting bias with respect to sensitive attributessuch as race and gender, which can lead to life-altering outcomes (e.g.,demographic bias in facial recognition software used to arrest a suspect). Wepropose a robust optimization problem, which we demonstrate can improvefairness in several datasets, both synthetic and real-world, using an affinelinear model. Leveraging second order information, we are able to find asolution to our optimization problem more efficiently than a purely first ordermethod.

Fast & Fair: Efficient Second-Order Robust Optimization for Fairness in Machine Learning