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In this paper we suggest NICE: a new algorithm to generate counterfactual explanations for heterogeneous tabular data.
“The use of ranks to avoid the assumption of normality implicit in the analysis of variance”
Milton Friedman · 1937
Earlier work this paper cites.
“A comparison of alternative tests of significance for the problem of m rankings”
Milton Friedman · 1940
Earlier work this paper cites.
“The magical number seven, plus or minus two: Some limits on our capacity for processing information.”
George Miller · 1956
Earlier work this paper cites.
“Distribution-free multiple comparisons”
Peter Nemenyi · 1962
Earlier work this paper cites.
“An Act to amend the Federal Deposit Insurance Act to require insured banks to maintain certain records, to require that certain transactions in U.S. currency be reported to the Department of the Treasury, and for other purposes.”, 1970
United States Congress · 1970
Earlier work this paper cites.
“Family resemblance, conceptual cohesiveness, and category construction”
Douglas Medin, William Wattenmaker and Sarah Hampson · 1987
Earlier work this paper cites.
“Nonlinear principal component analysis using autoassociative neural networks”
Mark Kramer · 1991
Earlier work this paper cites.
“Improved heterogeneous distance functions”
D Wilson and Tony Martinez · 1997
Earlier work this paper cites.
“A Case-Based Explanation System for Black-Box Systems”
Conor Nugent and Pádraig Cunningham · 2005
Earlier work this paper cites.
“Counterfactuals and explanation”
Boris Kment · 2006
Earlier work this paper cites.
“Uses of artificial intelligence in the Brazilian customs fraud detection system”
Luciano Digiampietri, Norton Roman, Luis Meira, Jorge Filho, Cristiano Ferreira, Andreia Kondo, Everton Constantino, Rodrigo Rezende, Bruno Brandao and Helder Ribeiro · 2008
Earlier work this paper cites.
“Introduction to Algorithms - Third Edition”
T.H. Cormen, C.E. Leiserson, R.L. Rivest and C. Stein · 2009
Earlier work this paper cites.
“Gaining insight through case-based explanation”
Conor Nugent, Dónal Doyle and Pádraig Cunningham · 2009
Earlier work this paper cites.
“Transaction aggregation as a strategy for credit card fraud detection”
Christopher Whitrow, David Hand, Piotr Juszczak, David Weston and Niall Adams · 2009
Earlier work this paper cites.
“The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature”
Eric Ngai, Yong Hu, Yiu Wong, Yijun Chen and Xin Sun · 2011
Earlier work this paper cites.
“Scikit-learn: Machine Learning in Python”
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay · 2011
Earlier work this paper cites.
“Counterfactuals”
David Lewis · 2013
Earlier work this paper cites.
“Explaining Data-Driven Document Classifications”
David Martens and Foster Provost · 2014
Earlier work this paper cites.
“On mining latent treatment patterns from electronic medical records”
Zhengxing Huang, Wei Dong, Peter Bath, Lei Ji and Huilong Duan · 2015
Earlier work this paper cites.
“Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research”
Stefan Lessmann, Bart Baesens, Hsin-Vonn Seow and Lyn Thomas · 2015
Earlier work this paper cites.
“Explaining explanation”
David-Hillel Ruben · 2015
Earlier work this paper cites.
“Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)”, 2016
European Parliament · 2016
Earlier work this paper cites.
““Why Should I Trust You?”: Explaining the Predictions of Any Classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
Earlier work this paper cites.
“Chapter 19 - Machine Learning in Healthcare”
Alison Callahan and Nigam. Shah · 2017
Earlier work this paper cites.
“Towards a rigorous science of interpretable machine learning”
Finale Doshi-Velez and Been Kim · 2017
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“Inverse Classification for Comparison-based Interpretability in Machine Learning”
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard and Marcin Detyniecki · 2017
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“A Unified Approach to Interpreting Model Predictions”
Scott. Lundberg and Su-In Lee · 2017
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“PMLB: a large benchmark suite for machine learning evaluation and comparison”
Randal Olson, William La, Patryk Orzechowski, Ryan Urbanowicz and Jason Moore · 2017
Cited alongside, same era.
“Explanations based on the missing: Towards contrastive explanations with pertinent negatives”
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam and Payel Das · 2018
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“Multi-objective counterfactual explanations”
Susanne Dandl, Christoph Molnar, Martin Binder and Bernd Bischl · 2020
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“Instance-Based Counterfactual Explanations for Time Series Classification”
Eoin Delaney, Derek Greene and Mark. Keane · 2020
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“Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach”
Carlos Fernández-Loría, Foster. Provost and Xintian Han · 2020
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“Fostering Human Agency: A Process for the Design of User-Centric XAI Systems”
Maximilian Förster, Mathias Klier, Kilian Kluge and Irina Sigler · 2020
Later among the works it cites.
“On cognitive preferences and the plausibility of rule-based models”
Johannes Fürnkranz, Tomáš Kliegr and Heiko Paulheim · 2020
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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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“Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)”
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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
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“Explanation in artificial intelligence: Insights from the social sciences”
Tim Miller · 2018
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“Anchors: High-Precision Model-Agnostic Explanations”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2018
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“Counterfactual explanations without opening the black box: Automated decisions and the GDPR”
Sandra Wachter, Brent Mittelstadt and Chris Russell · 2018
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“Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI”
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“DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization”
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“Model-agnostic counterfactual explanations for consequential decisions”
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“A survey of algorithmic recourse: definitions, formulations, solutions, and prospects”
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“Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)”
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“On Counterfactual Explanations under Predictive Multiplicity”
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“A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C”
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“Explainability fact sheets: a framework for systematic assessment of explainable approaches”
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“Value-added tax fraud detection with scalable anomaly detection techniques”
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“Counterfactual Explanations for Machine Learning: A Review”
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“Capturing Users’ Reality: A Novel Approach to Generate Coherent Counterfactual Explanations”
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“If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques”
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“A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data”
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