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Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
Kevin McCloskey, Ankur Taly, Federico Monti +2
Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur…
Counterfactual Fairness in Text Classification through Robustness
Sahaj Garg, Vincent Perot, Nicole Limtiaco +3
In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the exampl…
A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values
Mukund Sundararajan, Ankur Taly
Local explanation methods, also known as attribution methods, attribute a deep network's prediction to its input (cf. Baehrens et al. (2010)). We respond to the claim from Adebayo…
Did the Model Understand the Question?
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan +1
We analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text. Using the notion of \emph{attribution} (wo…
It was the training data pruning too!
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan +1
We study the current best model (KDG) for question answering on tabular data evaluated over the WikiTableQuestions dataset. Previous ablation studies performed against this model a…