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Feature-Wise Bias Amplification
[article]
2019
arXiv
pre-print
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. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the importance of moderately-predictive "weak" features if insufficient training data is available. This overestimation gives rise to feature-wise bias amplification -- a previously
arXiv:1812.08999v2
fatcat:dbdvoocw7faxjcedpy7j4aggku