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Recent papers in explainable AI have made a compelling case for counterfactual modes of explanation.
Foundations of the theory of signs
Charles William Morris · 1938
Earlier work this paper cites.
Philosophical investigations
Ludwig Wittgenstein · 1958
Earlier work this paper cites.
Opening up the black box of artificial neural networks
MT Spining, JA Darsey, BG Sumpter, and DW Nold · 1994
Earlier work this paper cites.
Survey and critique of techniques for extracting rules from trained artificial neural networks
Robert Andrews, Joachim Diederich, and Alan B Tickle · 1995
Earlier work this paper cites.
Statistical modeling: The two cultures
Leo Breiman · 2001
Earlier work this paper cites.
Learning deep architectures for ai
Yoshua Bengio et al · 2009
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
Earlier work this paper cites.
Building high-level features using large scale unsupervised learning
Quoc V Le · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Cited alongside, same era.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
Cited alongside, same era.
How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Jenna Burrell · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang · 2016
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Later among the works it cites.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
Later among the works it cites.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
Later among the works it cites.
Critical challenges for the visual representation of deep neural networks
Kieran Browne, Ben Swift, and Henry Gardner · 2018
Later among the works it cites.
Machine learning, social learning and the governance of self-driving cars
Jack Stilgoe · 2018
Later among the works it cites.
Did you hear that? adversarial examples against automatic speech recognition
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Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei · 2016
Cited alongside, same era.
Explanation and justification in machine learning: A survey
Or Biran and Courtenay Cotton · 2017
Cited alongside, same era.
Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
Cited alongside, same era.
Distilling a neural network into a soft decision tree
Geoffrey Hinton and Nicholas Frosst · 2017
Cited alongside, same era.
Tom Brown, Dandelion Mane, Aurko Roy, Martin Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
Moustafa Alzantot, Bharathan Balaji, and Mani Srivastava · 2018
Later among the works it cites.
The building blocks of interpretability
Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev · 2018
Later among the works it cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
Later among the works it cites.
One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
Later among the works it cites.
Connecting the digital and physical world: Improving the robustness of adversarial attacks
Steve TK Jan, Joseph Messou, Yen-Chen Lin, Jia-Bin Huang, and Gang Wang · 2019
Later among the works it cites.
Turing-NLG: A 17-billion-parameter language model by Microsoft, February 2020
Corby Rosset · 2020
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