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Interpretability is an elusive but highly sought-after characteristic of modern machine learning methods.
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Peter Lipton · 1990
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Jonathan Koehler · 1996
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SD Bay and MJ Pazzani · 1999
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Geoffrey Webb, Shane Butler and Douglas Newlands · 2003
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Petra Novak, Nada Lavra\$\$vc and Goeffrey Webb · 2009
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“Rules for contrast sets”
Paulo. Azevedo · 2010
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“Explanation and Abductive Inference”
Tania Lombrozo · 2012
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“Explanation and Justification in Machine Learning: A Survey”
Or Biran and Courtenay Cotton · 2017
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“Social Attribution and Explanation”
Denis Hilton · 2017
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“A unified approach to interpreting model predictions”
Scott Lundberg and Su-In Lee · 2017
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“Masked autoregressive flow for density estimation”
George Papamakarios, Theo Pavlakou and Iain Murray · 2017
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Ramprasaath. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh and Dhruv Batra · 2017
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Karen Simonyan, Andrea Vedaldi and Andrew Zisserman · 2013
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“Variational Inference with Normalizing Flows”
Danilo Rezende and Shakir Mohamed · 2015
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“Density estimation using real nvp”
Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio · 2016
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“Interpretable Decision Sets: A Joint Framework for Description and Prediction”
Himabindu Lakkaraju, Stephen Bach and L Jure · 2016
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“"Why Should I Trust You?": Explaining the Predictions of Any Classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
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“A causal framework for explaining the predictions of black-box sequence-to-sequence models”
David Alvarez-Melis and Tommi. Jaakkola · 2017
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“Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR”
Sandra Wachter, Brent Mittelstadt and Chris Russell · 2017
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“Towards Robust Interpretability with Self-explaining Neural Networks”
David Alvarez-Melis and Tommi Jaakkola · 2018
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“Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)”
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler and Fernanda Viegas · 2018
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“Making Algorithms Trustworthy: What Can Statistical Science Contribute to Transparency, Explanation and Validation?” Thirty-second Conference on Neural Information Processing Systems (NeurIPS), 2018
David Spiegelhalter · 2018
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“Contrastive explanations with local foil trees”
Jasper van Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis and Mark Neerincx · 2018
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“Contrastive Explanation: A Structural-Model Approach”
Tim Miller · 2019
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“Explanation in artificial intelligence: Insights from the social sciences”
Tim Miller · 2019
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