A confidence-based approach for balancing fairness and accuracy

B Fish, J Kun, ÁD Lelkes - Proceedings of the 2016 SIAM international …, 2016 - SIAM
We study three classical machine learning algorithms in the context of algorithmic fairness:
adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain …
被引用次数：272 相关文章

A Confidence-Based Approach for Balancing Fairness and Accuracy

B Fish, J Kun, ÁD Lelkes - arXiv e-prints, 2016 - ui.adsabs.harvard.edu
We study three classical machine learning algorithms in the context of algorithmic fairness:
adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain …

A Confidence-Based Approach for Balancing Fairness and Accuracy

B Fish, J Kun, ÁD Lelkes - arXiv preprint arXiv:1601.05764, 2016 - arxiv.org
We study three classical machine learning algorithms in the context of algorithmic fairness:
adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain …

[引用][C] A Confidence-Based Approach for Balancing Fairness and Accuracy

B Fish, J Kun, A Lelkes - research.google
A Confidence-Based Approach for Balancing Fairness and Accuracy Jump to Content
Research Research Who we are Back to Who we are menu Defining the technology of today …

[引用][C] A Confidence-Based Approach for Balancing Fairness and Accuracy

B Fish, J Kun, A Lelkes - research.google
A Confidence-Based Approach for Balancing Fairness and Accuracy Jump to Content
Research Research Who we are Back to Who we are menu Defining the technology of today …