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We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations.
Extracting tree-structured representations of trained networks
Mark Craven and Jude W. Shavlik · 1995
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Storage of features, conjunctions, and objects in visual working memory
Edward K Vogel, Geoffrey F Woodman, and Steven J Luck · 2001
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The capacity of visual short-term memory is set both by visual information load and by number of objects
George A Alvarez and Patrick Cavanagh · 2004
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Credit rating analysis with support vector machines and neural networks: a market comparative study
Zan Huang, Hsinchun Chen, Chia-Jung Hsu, Wun-Hwa Chen, and Soushan Wu · 2004
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Comprehensible credit scoring models using rule extraction from support vector machines
David Martens, Bart Baesens, Tony Van Gestel, and Jan Vanthienen · 2007
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Consumer credit-risk models via machine-learning algorithms
Amir E Khandani, Adlar J Kim, and Andrew W Lo · 2010
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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, and Bart Baesens · 2011
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Prototype selection for interpretable classification
Jacob Bien and Robert Tibshirani · 2011
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Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal
Raquel Flórez López and Juan Manuel Ramon-Jeronimo · 2015
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Demonstrating non-inferiority of easy interpretable methods for insolvency prediction
Lennart Obermann and Stephan Waack · 2015
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Selection of support vector machines based classifiers for credit risk domain
Paulius Danenas and Gintautas Garsva · 2015
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European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Credit scoring and its applications , volume 2
Lyn Thomas, Jonathan Crook, and David Edelman · 2017
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Incorporating sequential information in bankruptcy prediction with predictors based on markov for discrimination
Andrey Volkov, Dries F Benoit, and Dirk Van den Poel · 2017
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An improved credit card users default prediction model based on ripper
Pu Xu, Zhijun Ding, and MeiQin Pan · 2017
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Regulating fintech lending
Matthew A Bruckner · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, and Fosca Giannotti · 2018
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Francisco Louzada, Anderson Ara, and Guilherme B Fernandes · 2016
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Interpretable multiclass models for corporate credit rating capable of expressing doubt
Lennart Obermann and Stephan Waack · 2016
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent D. Mittelstadt, and Chris Russell · 2017
Cited alongside, same era.
"why should I trust you?": Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin
Cited in the paper.
Model-agnostic interpretability of machine learning
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin
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Interpretable Machine Learning
Christoph Molnar · 2018
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Credit risk analysis using machine and deep learning models
Peter Martey Addo, Dominique Guegan, and Bertrand Hassani · 2018
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