1 citations · 1 across the 1 of their papers we have counts for
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Feature-Wise Bias Amplification
Klas Leino, Emily Black, Matt Fredrikson +2
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.…
Supervising Feature Influence
Shayak Sen, Piotr Mardziel, Anupam Datta +1
Causal influence measures for machine learnt classifiers shed light on the reasons behind classification, and aid in identifying influential input features and revealing their bias…
Influence-Directed Explanations for Deep Convolutional Networks
Klas Leino, Shayak Sen, Anupam Datta +2
We study the problem of explaining a rich class of behavioral properties of deep neural networks. Distinctively, our influence-directed explanations approach this problem by peerin…
Debugging Machine Learning Tasks
Aleksandar Chakarov, Aditya Nori, Sriram Rajamani +2
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of co…