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Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another.
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Explaining image classifiers by counterfactual generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2018
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A survey of methods for explaining black box models
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A benchmark for interpretability methods in deep neural networks
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Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Game Theory and Mechanism Design , volume 4
Yadati Narahari · 2014
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Sobol’ indices and Shapley value
Art B Owen · 2014
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Very deep convolutional networks for large-scale image recognition
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The mythos of model interpretability
Zachary C Lipton · 2018
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RISE: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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INVASE: Instance-wise variable selection using neural networks
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2018
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Generative image inpainting with contextual attention
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2018
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Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
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Kjersti Aas, Martin Jullum, and Anders Løland · 2019
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Explaining an image classifier’s decisions using generative models
Chirag Agarwal and Anh Nguyen · 2019
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Learning about an exponential amount of conditional distributions
Mohamed Belghazi, Maxime Oquab, and David Lopez-Paz · 2019
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Learning explainable models using attribution priors
Gabriel Erion, Joseph D Janizek, Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2019
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Understanding deep networks via extremal perturbations and smooth masks
Ruth Fong, Mandela Patrick, and Andrea Vedaldi · 2019
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Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye, Ilya Feige, and Colin Rowat · 2019
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Please stop permuting features: An explanation and alternatives
Giles Hooker and Lucas Mentch · 2019
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Feature relevance quantification in explainable AI: A causality problem
Dominik Janzing, Lenon Minorics, and Patrick Blöbaum · 2019
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Human evaluation of models built for interpretability
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J Gershman, and Finale Doshi-Velez · 2019
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Explaining black box decisions by Shapley cohort refinement
Masayoshi Mase, Art B Owen, and Benjamin Seiler · 2019
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The explanation game: Explaining machine learning models with cooperative game theory
Luke Merrick and Ankur Taly · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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CXPlain: Causal explanations for model interpretation under uncertainty
Patrick Schwab and Walter Karlen · 2019
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The many Shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2019
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Infomask: Masked variational latent representation to localize chest disease
Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh, and Yoshua Bengio · 2019
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José MF Moura, and Peter Eckersley · 2020
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Understanding global feature contributions with additive importance measures
Ian Covert, Scott Lundberg, and Su-In Lee · 2020
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Shapley-based explainability on the data manifold
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Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf · 2020
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Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
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Counterfactual explanations for machine learning: A review
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Efficient nonparametric statistical inference on population feature importance using Shapley values
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Attribution in scale and space
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Explaining predictive models using Shapley values and non-parametric vine copulas
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