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Apr, 2024
利用公平速度递归改进神经网络的公平性
Enhancing Fairness in Neural Networks Using FairVIC
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Charmaine Barker, Daniel Bethell, Dimitar Kazakov
TL;DR
通过在训练阶段解决内在偏差,FairVIC方法提高神经网络的公平性,不依赖于保护性特征进行预测,从而在不牺牲模型准确性的情况下显著提升公平度。
Abstract
Mitigating
bias
in
automated decision-making
systems, specifically
deep learning models
, is a critical challenge in achieving
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