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To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed.
Uci machine learning repository
Ronny Kohavi and Barry Becker · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Causality
Judea Pearl · 2009
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Diederik P Kingma and Max Welling · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Counterfactual explanations without opening the black box: Automated decisions and the gpdr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Alessandro Magrini, Stefano Di Blasi, and Federico Mattia Stefanini · 2017
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Scott M Lundberg and Su-In Lee · 2017
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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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Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh · 2019
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Generative counterfactual introspection for explainable deep learning
Shusen Liu, Bhavya Kailkhura, Donald Loveland, and Yong Han · 2019
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Explaining visual models by causal attribution
Álvaro Parafita and Jordi Vitrià · 2019
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Face: Feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Arnaud Van Looveren and Janis Klaise · 2019
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Efficient search for diverse coherent explanations
Chris Russell · 2019
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http://www.bnlearn.com/bnrepository/ sangiovese
Bayesian network repository
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https://docs.seldon.io/projects/alibi alibi
Algorithms for monitoring and explaining machine learning models
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The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2020
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Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2020
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