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The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way.
Causation
David Lewis · 1974
Earlier work this paper cites.
Contrastive explanation
Peter Lipton · 1990
Earlier work this paper cites.
A class of fast gaussian binomial filters for speech and image processing
Richard A Haddad and Ali N Akansu · 1991
Earlier work this paper cites.
The psychology of counterfactual thinking
David R Mandel, Denis J Hilton, and Patrizia Ed Catellani · 2005
Earlier work this paper cites.
Quick shift and kernel methods for mode seeking
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
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Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
Earlier work this paper cites.
The mythos of model interpretability
Zachary C Lipton · 2016
Earlier work this paper cites.
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David Martens and Foster Provost · 2016
Earlier work this paper cites.
Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Why should I trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
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David Gunning · 2017
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Sandra Wachter, Brent Mittelstadt, and Luciano Floridi · 2017
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David Alvarez-Melis and Tommi S Jaakkola · 2018
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Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
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Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata · 2018
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Scott M Lundberg and Su-In Lee · 2017
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Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
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Daizhuo Chen, Samuel P Fraiberger, Robert Moakler, and Foster Provost · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
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Tim Miller · 2018
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The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2019
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Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2019
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Yanou Ramon, David Martens, Foster Provost, and Theodoros Evgeniou · 2019
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Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Explaining data-driven decisions made by ai systems: The counterfactual approach
Carlos Fernandez, Foster Provost, and Xintian Han · 2020
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ImageNet · 2020
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