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Counterfactual explanations can be obtained by identifying the smallest change made to a feature vector to qualitatively influence a prediction; for example, from 'loan rejected' to 'awarded' or from 'high risk of cardiovascular disease' to 'low risk'.
On decompositional algorithms for uniform sampling from n-spheres and n-balls
Radoslav Harman and Vladimír Lacko. 2010 · 2010
Earlier work this paper cites.
Sex and gender differences in health
Vera Regitz-Zagrosek. 2012 · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2015 · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models. In Advances in neural information processing systems . 3483–3491
Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015 · 2015
Earlier work this paper cites.
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. 2017 · 2017
Earlier work this paper cites.
Generalized inverse classification. In Proceedings of the 2017 SIAM International Conference on Data Mining . SIAM, 162–170
Michael T Lash, Qihang Lin, Nick Street, Jennifer G Robinson, and Jeffrey Ohlmann. 2017 · 2017
Earlier work this paper cites.
Inverse Classification for Comparison-based Interpretability in Machine Learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017 · 2017
Earlier work this paper cites.
Interpretable predictions of tree-based ensembles via actionable feature tweaking. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . ACM, 465–474
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017 · 2017
Cited alongside, same era.
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf. 2017 · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 1171–1180
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017 · 2017
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian. 2018 · 2018
Cited alongside, same era.
Counterfactual explanations without opening the black box: automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017 · 2018
Later among the works it cites.
A reductions approach to fair classification. In ICML
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. 2019 · 2019
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Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019 · 2019
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Issues with post-hoc counterfactual explanations: a discussion
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, and Marcin Detyniecki. 2019 · 2019
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Towards User Empowerment
Martin Pawelczyk, Johannes Haug, Klaus Broelemann, and Gjergji Kasneci. 2019 · 2019
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Interpretable Credit Application Predictions With Counterfactual Explanations
Rory Mc Grath, Luca Costabello, Chan Le Van, Paul Sweeney, Farbod Kamiab, Zhao Shen, and Freddy Lecue. 2018 · 2018
Cited alongside, same era.
Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction. In Proceedings of the 2018 World Wide Web Conference . International World Wide Web Conferences Steering Committee, 903–912
Nina Grgic-Hlaca, Elissa M Redmiles, Krishna P Gummadi, and Adrian Weller. 2018 · 2018
Cited alongside, same era.
Variational Autoencoder with Arbitrary Conditioning
Oleg Ivanov, Michael Figurnov, and Dmitry Vetrov. 2018 · 2018
Cited alongside, same era.
Handling incomplete heterogeneous data using VAEs
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera. 2018 · 2018
Cited alongside, same era.
Efficient Search for Diverse Coherent Explanations. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM FAT, 20–28
Christopher Russell. 2019 · 2019
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Actionable recourse in linear classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM, 10–19
Berk Ustun, Alexander Spangher, and Yang Liu. 2019 · 2019
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