Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.02012 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917558217605120 |
|---|---|
| author | Minch, Allen Vu, Hung Anh Warren, Anne Marie |
| author_facet | Minch, Allen Vu, Hung Anh Warren, Anne Marie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast & Fair: Efficient Second-Order Robust Optimization for Fairness in Machine Learning Minch, Allen Vu, Hung Anh Warren, Anne Marie Machine Learning Computers and Society Numerical Analysis 65F10, 65F22, 65K05, 90C47 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. |
| title | Fast & Fair: Efficient Second-Order Robust Optimization for Fairness in Machine Learning |
| topic | Machine Learning Computers and Society Numerical Analysis 65F10, 65F22, 65K05, 90C47 |
| url | https://arxiv.org/abs/2401.02012 |