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Oct, 2021
数据修复是否能实现公平模型?通过筛选上下文公平数据减少模型偏差
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias
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Sharat Agarwal, Sumanyu Muku, Saket Anand, Chetan Arora
TL;DR
本文提出了一种利用数据修复算法解决数据集中的语境偏差问题的解决方案,该算法可以通过平衡受保护属性的各种类别的共同出现率来筛选样本,从而训练出公平的模型,而不会牺牲模型整体性能。
Abstract
Contextual information is a valuable cue for
deep neural networks
(DNNs) to learn better representations and improve accuracy. However,
co-occurrence bias
in the training dataset may hamper a DNN model's generali
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