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Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favorable outcomes in the future (e.g., insurance approval).
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
Eoin M. Kenny and Mark T. Keane. 2020 · 2009
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Causality
Judea Pearl. 2009 · 2009
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Consumer credit-risk models via machine-learning algorithms
Amir E. Khandani, Adlar J. Kim, and Andrew W. Lo. 2010 · 2010
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Criminal justice forecasts of risk: A machine learning approach
Richard Berk. 2012 · 2012
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Intelligible models for classification and regression. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
Yin Lou, Rich Caruana, and Johannes Gehrke. 2012 · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton. 2012 · 2012
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Auto-encoding variational bayes. In International Conference on Learning Representations
Diederik P Kingma and Max Welling. 2013 · 2013
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Tensorflow: A system for large-scale machine learning. In 12th { \{ USENIX } \}
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks"
Surya Mattu Julia Angwin, Jeff Larson and Lauren Kirchner. 2016 · 2016
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Licon: A linear weighting scheme for the contribution ofinput variables in deep artificial neural networks. In CIKM
Gjergji Kasneci and Thomas Gottron. 2016 · 2016
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Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) . ACM
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Dheeru Dua and Casey Graff. 2017 · 2017
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Survey of machine learning algorithms for disease diagnostic
Meherwar Fatima, Maruf Pasha, et al · 2017
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Generalized inverse classification. In Proceedings of the 2017 SIAM International Conference on Data Mining . SIAM
Michael T Lash, Qihang Lin, Nick Street, Jennifer G Robinson, and Jeffrey Ohlmann. 2017 · 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 · 2017
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A unified approach to interpreting model predictions. In Conference on Neural Information Processing Systems (NeurIPS)
Scott Lundberg and Su-In Lee. 2017 · 2017
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Interpretable predictions of tree-based ensembles via actionable feature tweaking. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) . ACM
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017 · 2017
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Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter, B. Mittelstadt, and Chris Russell. 2017 · 2017
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Towards robust interpretability with self-explaining neural networks. In Conference on Neural Information Processing Systems (NeurIPS)
David Alvarez-Melis and Tommi S Jaakkola. 2018 · 2018
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian. 2018 · 2018
Cited alongside, same era.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. In Advances in Neural Information Processing Systems (NeurIPS)
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018 · 2018
Cited alongside, same era.
A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
Cited alongside, same era.
Making tree ensembles interpretable: A bayesian model selection approach. In International Conference on Artificial Intelligence and Statistics (AISTATS) . PMLR
Satoshi Hara and Kohei Hayashi. 2018 · 2018
Cited alongside, same era.
Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems . 1–15
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019 · 2019
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Multi-objective counterfactual explanations. In International Conference on Parallel Problem Solving from Nature . Springer, 448–469
Susanne Dandl, Christoph Molnar, Martin Binder, and Bernd Bischl. 2020 · 2020
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CRUDS: Counterfactual Recourse Using Disentangled Subspaces
Michael Downs, Jonathan L. Chu, Yaniv Yacoby, Finale Doshi-Velez, and Weiwei Pan. 2020 · 2020
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PRINCE: provider-side interpretability with counterfactual explanations in recommender systems. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM)
Azin Ghazimatin, Oana Balalau, Rishiraj Saha Roy, and Gerhard Weikum. 2020 · 2020
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 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. 2018 · 2018
Cited alongside, same era.
A gradient-based split criterion for highly accurate and transparent model trees. In Proceedings of the International Joint Conference on Artificial Intelligence IJCAI
Klaus Broelemann and Gjergji Kasneci. 2019 · 2019
Cited alongside, same era.
Interpreting tree ensembles with intrees
Houtao Deng. 2019 · 2019
Cited alongside, same era.
Explainable AI in industry. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, and Ankur Taly. 2019 · 2019
Cited alongside, same era.
Explaining classifiers with causal concept effect (cace)
Yash Goyal, Amir Feder, Uri Shalit, and Been Kim. 2019 · 2019
Cited alongside, same era.
Equalizing recourse across groups
Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, and Suresh Venkatasubramanian. 2019 · 2019
Cited alongside, same era.
Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019 · 2019
Cited alongside, same era.
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, and Hiroki Arimura. 2020 · 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. 2020b · 2020
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From local explanations to global understanding with explainable AI for trees
Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. 2020 · 2020
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Handling incomplete heterogeneous data using vaes
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera. 2020 · 2020
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Generative causal explanations of black-box classifiers. In Conference on Neural Information Processing Systems (NeurIPS)
Matthew O’Shaughnessy, Gregory Canal, Marissa Connor, Mark Davenport, and Christopher Rozell. 2020 · 2020
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Learning Model-Agnostic Counterfactual Explanations for Tabular Data. In Proceedings of The Web Conference 2020 (WWW) . ACM
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci. 2020a · 2020
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In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach. 2020 · 2020
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Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. In Conference on Neural Information Processing Systems (NeurIPS)
Kaivalya Rawal and Himabindu Lakkaraju. 2020 · 2020
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Counterfactual Explanations for Machine Learning: A Review
Sahil Verma, John Dickerson, and Keegan Hines. 2020 · 2020
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Getting a CLUE: A Method for Explaining Uncertainty Estimates. In International Conference on Learning Representations (ICLR)
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2021 · 2021
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Algorithmic Recourse: from Counterfactual Explanations to Interventions. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT)
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera. 2021 · 2021
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On the Connections between Counterfactual Explanations and Adversarial Examples
Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, and Himabindu Lakkaraju. 2021 · 2021
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Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. In International Conference on Artificial Intelligence and Statistics . PMLR, 1756–1764
Lisa Schut, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al · 2021
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A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
Ilia Stepin, Jose M Alonso, Alejandro Catala, and Martín Pereira-Fariña. 2021 · 2021
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