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

• URL: http://arxiv.org/abs/2401.02012v1
• Date: Thu, 4 Jan 2024 01:02:55 GMT
• Title: Fast & Fair: Efficient Second-Order Robust Optimization for Fairness in Machine Learning
• Authors: Allen Minch, Hung Anh Vu, Anne Marie Warren
• Abstract summary: This project explores adversarial training techniques to develop fairer Deep Neural Networks (DNNs) DNNs are susceptible to inheriting bias with respect to sensitive attributes such as race and gender, which can lead to life-altering outcomes. We propose a robust optimization problem, which we demonstrate can improve fairness in several datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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