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We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth.
Neural networks and the bias/variance dilemma
Stuart Geman, Elie Bienenstock, and René Doursat · 1992
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Yi Murphey, Hong Guo, and Lee Feldkamp · 2004
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Maciej A. Mazurowski, Piotr A. Habas, Jacek M. Zurada, Joseph Y. Lo, Jay A. Baker, and Georgia D. Tourassi · 2007
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B. C. Wallace, K. Small, C. E. Brodley, and T. A. Trikalinos · 2011
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Mateusz Buda, Atsuto Maki, and Maciej A. Mazurowski · 2017
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UCI machine learning repository, 2017
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If you’re not a white male, artificial intelligence’s use in healthcare could be dangerous
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The Implicit Bias of Gradient Descent on Separable Data
D. Soudry, E. Hoffer, M. Shpigel Nacson, S. Gunasekar, and N. Srebro · 2017
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Pierre Stock and Moustapha Cissé · 2017
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Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Implicit Bias of Gradient Descent on Linear Convolutional Networks
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