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Technical Challenges for Training Fair Neural Networks
[article]
2021
arXiv
pre-print
As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging). To respond to these concerns, the community has proposed and formalized various notions of fairness as well as methods for rectifying unfair behavior. While fairness constraints have been studied extensively for classical models, the effectiveness of methods for
arXiv:2102.06764v1
fatcat:nunewctjxjd73as3muubliiknq