17 citations · 24 across the 3 of their papers we have counts for
6 papers
Faithful Explanations for Deep Graph Models
Zifan Wang, Yuhang Yao, Chaoran Zhang +5
This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations f…
Consistent Counterfactuals for Deep Models
Emily Black, Zifan Wang, Matt Fredrikson +1
Counterfactual examples are one of the most commonly-cited methods for explaining the predictions of machine learning models in key areas such as finance and medical diagnosis. Cou…
Robust Models Are More Interpretable Because Attributions Look Normal
Zifan Wang, Matt Fredrikson, Anupam Datta
Recent work has found that adversarially-robust deep networks used for image classification are more interpretable: their feature attributions tend to be sharper, and are more conc…
Interpreting Interpretations: Organizing Attribution Methods by Criteria
Zifan Wang, Piotr Mardziel, Anupam Datta +1
Motivated by distinct, though related, criteria, a growing number of attribution methods have been developed tointerprete deep learning. While each relies on the interpretability o…
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.…
Influence in Classification via Cooperative Game Theory
Amit Datta, Anupam Datta, Ariel D. Procaccia +1
A dataset has been classified by some unknown classifier into two types of points. What were the most important factors in determining the classification outcome? In this work, we…