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Counterfactual examples are one of the most commonly-cited methods for explaining the predictions of machine learning models in key areas such as finance and medical diagnosis.
Predictive multiplicity in classification
Charles T. Marx, Flávio du Pin Calmon, and Berk Ustun · 1909
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Analysis of different norms and corresponding lipschitz constants for global optimization
Remigijus Paulavičius and Julius Žilinskas · 2006
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Estimation of the warfarin dose with clinical and pharmacogenetic data
International Warfarin Pharmacogenetics Consortium · 2009
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Intriguing properties of neural networks, 2013
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Early diagnosis of alzheimer’s disease with deep learning
Siqi Liu, Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis, and Dagan Feng · 2014
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
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European parliament and council of european union (2016) regulation (eu) 2016/679
GDPR · 2016
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Computer aided lung cancer diagnosis with deep learning algorithms
Wenqing Sun, Bin Zheng, and Wei Qian · 2016
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UCI machine learning repository
Dheeru Dua and Efi Karra Taniskidou · 2017
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A survey on deep learning in medical image analysis
Geert Litjens et. al · 2017
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Generalized inverse classification
Michael T Lash, Qihang Lin, Nick Street, Jennifer G Robinson, and Jeffrey Ohlmann · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Credit risk analysis using machine and deep learning models
Peter Addo, Dominique Guegan, and Bertrand Hassani · 2018
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Deep learning and medical diagnosis: A review of literature
Mihalj Bakator and Dragica Radosav · 2018
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Insurance 2030: The impact of ai on the future of insurance
Ramnath Balasubramanian et al · 2018
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Ead: elastic-net attacks to deep neural networks via adversarial examples
Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2018
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Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, et al · 2018
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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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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Local rule-based explanations of black box decision systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti · 2018
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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
Cited alongside, same era.
Influence-directed explanations for deep convolutional networks
Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, and Linyi Li · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Adversarial robustness toolbox v1.2.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian Molloy, and Ben Edwards · 2018
Cited alongside, same era.
Interpretable counterfactual explanations guided by prototypes
Arnaud Van Looveren and Janis Klaise · 2019
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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L. Yuille · 2019
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Managing the risks of machine learning and artificial intelligence models in the financial services industry, 2020
2020
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The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2020
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Kieran Browne and Ben Swift · 2020
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
Cited alongside, same era.
Leveraging deep learning with lda-based text analytics to detect automobile insurance fraud
Yibo Wang and Wei Xu · 2018
Cited alongside, same era.
Et-rnn: Applying deep learning to credit loan applications
Dmitrii Babaev et al · 2019
Cited alongside, same era.
Provable robustness of relu networks via maximization of linear regions
Francesco Croce, Maksym Andriushchenko, and Matthias Hein · 2019
Cited alongside, same era.
Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel · 2019
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
Cited alongside, same era.
Deep relu networks have surprisingly few activation patterns
Boris Hanin and David Rolnick · 2019
Cited alongside, same era.
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al · 2020
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Multi-objective counterfactual explanations
Susanne Dandl, Christoph Molnar, Martin Binder, and Bernd Bischl · 2020
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Timo Freiesleben · 2020
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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, and Isabel Valera · 2020
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Good counterfactuals and where to find them: A case-based technique for generating counterfactuals for explainable ai (xai)
Mark T Keane and Barry Smyth · 2020
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Model lifecycle transformation: How banks are unlocking efficiencies: Accenture, Dec 2020
Gordon et al. Merchant · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Learning model-agnostic counterfactual explanations for tabular data
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
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Face: feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2020
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Available at: https://pypi.org/project/tensorflow-determinism/
tensorflow-determinism Python package · 2020
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Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
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Smoothed geometry for robust attribution
Zifan Wang, Haofan Wang, Shakul Ramkumar, Matt Fredrikson, Piotr Mardziel, and Anupam Datta · 2020
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Linyi Yang, Eoin M Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, and Ruihai Dong · 2020
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Leave-one-out unfairness
Emily Black and Matt Fredrikson · 2021
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Fast geometric projections for local robustness certification
Aymeric Fromherz, Klas Leino, Matt Fredrikson, Bryan Parno, and Corina Păsăreanu · 2021
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Can i still trust you?: Understanding the impact of distribution shifts on algorithmic recourses, 2021
Kaivalya Rawal, Ece Kamar, and Himabindu Lakkaraju · 2021
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Boundary attributions provide normal (vector) explanations
Zifan Wang, Matt Fredrikson, and Anupam Datta · 2021
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