Ankur Taly

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A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms

Given that there are a variety of stakeholders involved in, and affected...
03/27/2021 ∙ by Ana Lucic, et al. ∙ 0 ∙ share

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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...
03/27/2021 ∙ by David Watson, et al. ∙ 0 ∙ share

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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...
09/17/2019 ∙ by Luke Merrick, et al. ∙ 0 ∙ share

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Explainable Machine Learning in Deployment

Explainable machine learning seeks to provide various stakeholders with ...
09/13/2019 ∙ by Umang Bhatt, et al. ∙ 15 ∙ share

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Finding Invariants in Deep Neural Networks

We present techniques for automatically inferring invariant properties o...
04/29/2019 ∙ by Divya Gopinath, et al. ∙ 0 ∙ share

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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...
11/27/2018 ∙ by Kevin McCloskey, et al. ∙ 0 ∙ share

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Counterfactual Fairness in Text Classification through Robustness

In this paper, we study counterfactual fairness in text classification, ...
09/27/2018 ∙ by Sahaj Garg, et al. ∙ 8 ∙ share

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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 ...
06/11/2018 ∙ by Mukund Sundararajan, et al. ∙ 0 ∙ share

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Did the Model Understand the Question?

We analyze state-of-the-art deep learning models for three tasks: questi...
05/14/2018 ∙ by Pramod Kaushik Mudrakarta, et al. ∙ 0 ∙ share

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It was the training data pruning too!

We study the current best model (KDG) for question answering on tabular ...
03/12/2018 ∙ by Pramod Kaushik Mudrakarta, et al. ∙ 0 ∙ share

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Abductive Matching in Question Answering

We study question-answering over semi-structured data. We introduce a ne...
09/10/2017 ∙ by Kedar Dhamdhere, et al. ∙ 0 ∙ share

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Gradients of Counterfactuals

Gradients have been used to quantify feature importance in machine learn...
11/08/2016 ∙ by Mukund Sundararajan, et al. ∙ 0 ∙ share

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Ankur Taly

Featured Co-authors

Maarten de Rijke

Adrian Weller

Joydeep Ghosh

José M. F. Moura

Q Vera Liao

Alex Beutel

Umang Bhatt

Federico Monti

Mukund Sundararajan

Ed H. Chi

Divya Gopinath

A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

The Explanation Game: Explaining Machine Learning Models with Cooperative Game Theory

Explainable Machine Learning in Deployment

Finding Invariants in Deep Neural Networks

Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry

Counterfactual Fairness in Text Classification through Robustness

A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values

Did the Model Understand the Question?

It was the training data pruning too!

Abductive Matching in Question Answering

Gradients of Counterfactuals