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The prevalence of machine learning models in various industries has led to growing demands for model interpretability and for the ability to provide meaningful recourse to users.
Causation
David K Lewis · 1973
Earlier work this paper cites.
Using the ADAP learning algorithm to forecast the onset of diabetes mellitus
Jack W Smith, J E Everhart, W C Dickson, W C Knowler, and R S Johannes · 1988
Earlier work this paper cites.
Causality: Models, Reasoning and Inference
Judea Pearl · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
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Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Application of machine learning algorithms to an online recruitment system
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Earlier work this paper cites.
Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models
N Tollenaar and P G M van der Heijden · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
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Emily Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2016
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“why should I trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris Metaxas · 2016
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2016
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Learning residual images for face attribute manipulation
Wei Shen and Rujie Liu · 2016
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
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Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A Efros, Oliver Wang, and Eli Shechtman · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal
Jifeng Wang, Xiang Li, Le Hui, and Jian Yang · 2017
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Compas, 2017
ProPublica · 2017
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Deep learning for healthcare: review, opportunities and challenges
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Cathy O’Neil · 2016
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Why a right to explanation of automated Decision-Making does not exist in the general data protection regulation
Sandra Wachter, Brent Mittelstadt, and Luciano Floridi · 2017
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Meaningful information and the right to explanation
Andrew D Selbst and Julia Powles · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 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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Credit risk analysis using machine and deep learning models
Peter Martey Addo, Dominique Guegan, and Bertrand Hassani · 2018
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 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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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2019
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Interpretable counterfactual explanations guided by prototypes
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FACE: Feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2019
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Ling Zhang, Chengjiang Long, Xiaolong Zhang, and Chunxia Xiao · 2019
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Learning to detect genuine versus posed pain from facial expressions using residual generative adversarial networks
M Tavakolian, C G Bermudez Cruces, and A Hadid · 2019
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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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