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Title: Fast & Fair: Efficient Second-Order Robust Optimization for Fairness in Machine Learning
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 with a purely first order method.  more » « less
Award ID(s):
2051019
PAR ID:
10542777
Author(s) / Creator(s):
; ;
Publisher / Repository:
SIAM
Date Published:
Journal Name:
SIAM Undergraduate Research Online
Volume:
17
ISSN:
2327-7807
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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