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We examine counterfactual explanations for explaining the decisions made by model-based AI systems.
Survey and critique of techniques for extracting rules from trained artificial neural networks
Robert Andrews, Joachim Diederich, and Alan B Tickle · 1995
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Extracting tree-structured representations of trained networks
Mark Craven and Jude W Shavlik · 1996
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Explanations from intelligent systems: Theoretical foundations and implications for practice
Shirley Gregor and Izak Benbasat · 1999
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Benchmarking state-of-the-art classification algorithms for credit scoring
Bart Baesens, Tony Van Gestel, Stijn Viaene, Maria Stepanova, Johan Suykens, and Jan Vanthienen · 2003
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Rule extraction from recurrent neural networks: A taxonomy and review
Henrik Jacobsson · 2005
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The differential use and effect of knowledge-based system explanations in novice and expert judgment decisions
Vicky Arnold, Nicole Clark, Philip A Collier, Stewart A Leech, and Steve G Sutton · 2006
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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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Handling missing values when applying classification models
Maytal Saar-Tsechansky and Foster Provost · 2007
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Contact personalization using a score understanding method
Vincent Lemaire, Raphael Féraud, and Nicolas Voisine · 2008
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Explaining classifications for individual instances
Marko Robnik-Šikonja and Igor Kononenko · 2008
Earlier work this paper cites.
How incorporating feedback mechanisms in a DSS affects DSS evaluations
Ujwal Kayande, Arnaud De Bruyn, Gary L Lilien, Arvind Rangaswamy, and Gerrit H Van Bruggen · 2009
Earlier work this paper cites.
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Foster Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, and Alan Murray · 2009
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Explaining instance classifications with interactions of subsets of feature values
Erik Štrumbelj, Igor Kononenko, and M Robnik Šikonja · 2009
Earlier work this paper cites.
An efficient explanation of individual classifications using game theory
Erik Štrumbelj and Igor Kononenko · 2010
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A taxonomy for generating explanations in recommender systems
Gerhard Friedrich and Markus Zanker · 2011
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Big data, analytics and the path from insights to value
Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S Hopkins, and Nina Kruschwitz · 2011
Earlier work this paper cites.
Predictive analytics in Information Systems research
Galit Shmueli and Otto R Koppius · 2011
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Private traits and attributes are predictable from digital records of human behavior
Michal Kosinski, David Stillwell, and Thore Graepel · 2013
Cited alongside, same era.
Data Science for Business: What you need to know about data mining and data-analytic thinking
Foster Provost and Tom Fawcett · 2013
Cited alongside, same era.
Explaining data-driven document classifications
David Martens and Foster Provost · 2014
Cited alongside, same era.
Machine learning for targeted display advertising: Transfer learning in action
Claudia Perlich, Brian Dalessandro, Troy Raeder, Ori Stitelman, and Foster Provost · 2014
Cited alongside, same era.
Understanding decisions driven by big data, 2014
Foster Provost · 2014
Cited alongside, same era.
Falling rule lists
Fulton Wang and Cynthia Rudin · 2015
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GPDR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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’it’s reducing a human being to a percentage’ perceptions of justice in algorithmic decisions
Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt · 2018
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Data-driven investment strategies for peer-to-peer lending: A case study for teaching data science
Maxime C Cohen, C Daniel Guetta, Kevin Jiao, and Foster Provost · 2018
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Explore, exploit, and explain: personalizing explainable recommendations with bandits
James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, and Rishabh Mehrotra · 2018
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Optimal multi-way number partitioning
Ethan L Schreiber, Richard E Korf, and Michael D Moffitt · 2018
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
Cited alongside, same era.
Explaining classification models built on high-dimensional sparse data
Julie Moeyersoms, Brian d’Alessandro, Foster Provost, and David Martens · 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.
Learning certifiably optimal rule lists for categorical data
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin · 2017
Cited alongside, same era.
Explanation and justification in machine learning: A survey
Or Biran and Courtenay Cotton · 2017
Cited alongside, same era.
Enhancing transparency and control when drawing data-driven inferences about individuals
Daizhuo Chen, Samuel P Fraiberger, Robert Moakler, and Foster Provost · 2017
Cited alongside, same era.
Explaining models: an empirical study of how explanations impact fairness judgment
Jonathan Dodge, Q Vera Liao, Yunfeng Zhang, Rachel KE Bellamy, and Casey Dugan · 2019
Later among the works it cites.
Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan · 2019
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Doubting the diagnosis: How artificial intelligence increases ambiguity during professional decision making
Sarah Lebovitz, Natalia Levina, and Hila Lifshitz-Assaf · 2019
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Good explanation for algorithmic transparency
Joy Lu, Dokyun DK Lee, Tae Wan Kim, and David Danks · 2019
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Interpretable machine learning, see 18.1 counterfactual explanations
Christoph Molnar · 2019
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Interpretable counterfactual explanations guided by prototypes
Arnaud Van Looveren and Janis Klaise · 2019
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The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2020
Closest in time.
Vice: visual counterfactual explanations for machine learning models
Oscar Gomez, Steffen Holter, Jun Yuan, and Enrico Bertini · 2020
Closest in time.
Counterfactual theories of causation
Peter Menzies and Helen Beebee · 2020
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Counterfactual explanation algorithms for behavioral and textual data
Yanou Ramon, David Martens, Foster Provost, and Theodoros Evgeniou · 2020
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Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
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