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Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model.
Hedonic prices and the demand for clean air
David Harrison and Daniel Rubinfeld · 1978
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Extracting tree-structured representations of trained neural networks
Mark W. Craven and Jude W. Shavlik · 1996
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A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu · 1996
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A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin de Silva, and John C. Langford · 2000
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How to Explain Individual Classification Decisions Motoaki Kawanabe
David Baehrens, Timon Schroeter, Stefan Harmeling, Katja Hansen, and Klaus-Robert Muller · 2010
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Counterfactual reasoning and learning systems: The example of computational advertising
Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson · 2013
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The Bayesian Case Model: A generative approach for case-based reasoning and prototype classification
Been Kim, Cynthia Rudin, and Julie A Shah · 2014
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Understanding classifier errors by examining influential neighbors
Mayank Kabra, Alice Robie, and Kristin Branson · 2015
Earlier work this paper cites.
A model explanation system
Ryan Turner · 2015
Cited alongside, same era.
Making tree ensembles interpretable
Satoshi Hara and Kohei Hayashi · 2016
Cited alongside, same era.
How we analyzed the compas recidivism algorithm
Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin · 2016
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.
Generalized inverse classification
Michael Lash, Qihang Lin, Nick Street, Jennifer Robinson, and Jeffrey Ohlmann · 2017
Cited alongside, same era.
The mythos of model interpretability
Zachary C. Lipton · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Local rule-based explanations of black box decision systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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To trust or not to trust a classifier
Heinrich Jiang, Been Kim, Melody Guan, and Maya Gupta · 2018
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Comparison-based inverse classification for interpretability in machine learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2018
Later among the works it cites.
Defining locality for surrogates in post-hoc interpretablity
Thibault Laugel, Xavier Renard, Marie-Jeanne Lesot, Christophe Marsala, and Marcin Detyniecki · 2018
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Please stop explaining black box models for high stakes decisions
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Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Towards robust interpretability with self-explaining neural networks
David Alvarez Melis and Tommi Jaakkola · 2018
Cited alongside, same era.
Cynthia Rudin · 2018
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.