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A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Given that there are a variety of stakeholders involved in, and affected...
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Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Necessity and sufficiency are the building blocks of all successful expl...
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The Explanation Game: Explaining Machine Learning Models with Cooperative Game Theory
Recently, a number of techniques have been proposed to explain a machine...
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Explainable Machine Learning in Deployment
Explainable machine learning seeks to provide various stakeholders with ...
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Finding Invariants in Deep Neural Networks
We present techniques for automatically inferring invariant properties o...
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Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
Deep neural networks have achieved state of the art accuracy at classify...
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Counterfactual Fairness in Text Classification through Robustness
In this paper, we study counterfactual fairness in text classification, ...
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A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values
Local explanation methods, also known as attribution methods, attribute ...
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Did the Model Understand the Question?
We analyze state-of-the-art deep learning models for three tasks: questi...
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It was the training data pruning too!
We study the current best model (KDG) for question answering on tabular ...
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Abductive Matching in Question Answering
We study question-answering over semi-structured data. We introduce a ne...
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Gradients of Counterfactuals
Gradients have been used to quantify feature importance in machine learn...
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