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Counterfactual explanations are viewed as an effective way to explain machine learning predictions.
“Model-Agnostic Counterfactual Explanations for Consequential Decisions”
Amir-Hossein Karimi, Gilles Barthe, Borja Balle and Isabel Valera · 1905
Earlier work this paper cites.
“Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations”
Ramaravind Mothilal, Amit Sharma and Chenhao Tan · 1905
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“Explaining Deep Learning Models with Constrained Adversarial Examples”
Jonathan Moore, Nils Hammerla and Chris Watkins · 1906
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“Interpretable Counterfactual Explanations Guided by Prototypes”
Arnaud Looveren and Janis Klaise · 1907
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“On the generalized distance in statistics”, 1936
Prasanta Mahalanobis · 1936
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“The use of ranks to avoid the assumption of normality implicit in the analysis of variance”
Milton Friedman · 1937
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“A comparison of alternative tests of significance for the problem of m rankings”
Milton Friedman · 1940
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“Distribution-free Multiple Comparisons”
P. Nemenyi · 1963
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“Norm theory: Comparing reality to its alternatives.”
Daniel Kahneman and Dale Miller · 1986
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“Lymphography Data Set”, 1988
M. Zwitter and M. Soklic · 1988
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“Contrastive Explanation”
Peter Lipton · 1990
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“Approximation capabilities of multilayer feedforward networks”
Kurt Hornik · 1991
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“Multiattribute data presentation and human judgment: A cognitive fit perspective”
Narayan Umanath and Iris Vessey · 1994
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“Providing decisional guidance for multicriteria decision making in groups”
Moez Limayem and Gerardine DeSanctis · 2000
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“Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach”, 2020
Carlos Fernández-Loría, Foster Provost and Xintian Han · 2001
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“DSS effectiveness in marketing resource allocation decisions: Reality vs. perception”
Gary Lilien, Arvind Rangaswamy, Gerrit Van and Katrin Starke · 2004
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“The differential use and effect of knowledge-based system explanations in novice and expert judgment decisions”
Vicky Arnold et al · 2006
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“Statistical comparisons of classifiers over multiple data sets”
Janez Demšar · 2006
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“Comprehensible credit scoring models using rule extraction from support vector machines”
David Martens, Bart Baesens, Tony Van and Jan Vanthienen · 2007
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“Using data mining to predict secondary school student performance”
Paulo Cortez and Aliceçalves Silva · 2008
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“Understanding Support Vector Machine Classifications via a Recommender System-Like Approach.”
David Barbella et al · 2009
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“How incorporating feedback mechanisms in a DSS affects DSS evaluations”
Ujwal Kayande et al · 2009
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“The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients”
I-Cheng Yeh and Che-hui Lien · 2009
Earlier work this paper cites.
“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“Failure analysis of parameter-induced simulation crashes in climate models”
D.. Lucas et al · 2013
Earlier work this paper cites.
“Review on methods to fix number of hidden neurons in neural networks”
K Sheela and Subramaniam Deepa · 2013
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“Metaheuristics in large-scale global continues optimization: A survey”
Sedigheh Mahdavi, Mohammad Shiri and Shahryar Rahnamayan · 2014
Cited alongside, same era.
“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
Martin et al · 2015
Cited alongside, same era.
“ImageNet Large Scale Visual Recognition Challenge”
Olga Russakovsky et al · 2015
Cited alongside, same era.
“” Why should i trust you?” Explaining the predictions of any classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
Cited alongside, same era.
“UCI Machine Learning Repository”, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
“Inverse Classification for Comparison-based Interpretability in Machine Learning”
“Generating counterfactual and contrastive explanations using SHAP”
Shubham Rathi · 2019
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Shubham Sharma, Jette Henderson and Joydeep Ghosh · 2019
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“Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: a survey”
Igor Škrjanc et al · 2019
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“Fair-by-design explainable models for prediction of recidivism”
Eduardo Soares and Plamen Angelov · 2019
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“Explainable density-based approach for self-driving actions classification”
Eduardo Soares et al · 2019
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Thibault Laugel et al · 2017
Cited alongside, same era.
“A unified approach to interpreting model predictions”
Scott Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Wojciech Samek, Thomas Wiegand and Klaus-Robert Müller · 2017
Cited alongside, same era.
“Counterfactual explanations without opening the black box: Automated decisions and the GDPR”
Sandra Wachter, Brent Mittelstadt and Chris Russell · 2017
Cited alongside, same era.
“Toward anthropomorphic machine learning”
Plamen Angelov and Xiaowei Gu · 2018
Cited alongside, same era.
“’It’s Reducing a Human Being to a Percentage’ Perceptions of Justice in Algorithmic Decisions”
Reuben Binns et al · 2018
Cited alongside, same era.
“Improving the explainability of Random Forest classifier–user centered approach”
Dragutin Petkovic, Russ Altman, Mike Wong and Arthur Vigil · 2018
Cited alongside, same era.
Later among the works it cites.
“Measurable counterfactual local explanations for any classifier”
Adam White and Artur’Avila Garcez · 2019
Later among the works it cites.
“Interpreting Machine Learning Models and Application of Homotopy Methods”, 2019
Roozbeh Yousefzadeh · 2019
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“Convex Density Constraints for Computing Plausible Counterfactual Explanations”
André Artelt and Barbara Hammer · 2020
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“Explainable ai for interpretable credit scoring”
Lara Demajo, Vince Vella and Alexiei Dingli · 2020
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“Highly interpretable hierarchical deep rule-based classifier”
Xiaowei Gu and Plamen Angelov · 2020
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“DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization”
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi and Hiroki Arimura · 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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“A survey of algorithmic recourse: definitions, formulations, solutions, and prospects”
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf and Isabel Valera · 2020
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“Machine learning for enterprises: Applications, algorithm selection, and challenges”
In Lee and Yong Shin · 2020
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“Explainable AI: A Review of Machine Learning Interpretability Methods”
Pantelis Linardatos, Vasilis Papastefanopoulos and Sotiris Kotsiantis · 2020
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“Counterfactual Theories of Causation”
Peter Menzies and Helen Beebee · 2020
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“Handling incomplete heterogeneous data using vaes”
Alfredo Nazabal, Pablo Olmos, Zoubin Ghahramani and Isabel Valera · 2020
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“Explainability fact sheets: a framework for systematic assessment of explainable approaches”
Kacper Sokol and Peter Flach · 2020
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“Counterfactual Explanations for Machine Learning: A Review”
Sahil Verma, John Dickerson and Keegan Hines · 2020
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“A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging”
Mehmet Gulum, Christopher Trombley and Mehmed Kantardzic · 2021
Closest in time.
“gbt-hips: Explaining the classifications of gradient boosted tree ensembles”
Julian Hatwell, Mohamed Gaber and R Azad · 2021
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Mark Keane, Eoin Kenny, Eoin Delaney and Barry Smyth · 2021
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“Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–a critical review”
Dattatray Kute, Biswajeet Pradhan, Nagesh Shukla and Abdullah Alamri · 2021
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“Explaining Deep Learning-Based Driver Models”
Maria Lorente et al · 2021
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“Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set”
Roberto Porto, Jose Molina, Antonio Berlanga and Miguel Patricio · 2021
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