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As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs.
Generalized Additive Models
T. J. Hastie and R. J. Tibshirani · 1990
Earlier work this paper cites.
Model compression
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil · 2006
Earlier work this paper cites.
Automatically generating annotator rationales to improve sentiment classification
Y. Ainur, Y. Choi, and C. Cardie · 2010
Earlier work this paper cites.
An analytics approach for proactively combating voluntary attrition of employees
M. Singh, K. R. Varshney, J. Wang, A. Mojsilović, A. R. Gill, P. I. Faur, and R. Ezry · 2012
Earlier work this paper cites.
Do deep nets really need to be deep?
L. J. Ba and R. Caurana · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
Comprehensible classification models — a position paper
A. A. Freitas · 2014
Earlier work this paper cites.
Big data system for analyzing risky procurement entities
A. Dhurandhar, B. Graves, R. Ravi, G. Maniachari, and M. Ettl · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Earlier work this paper cites.
EU regulations on algorithmic decision-making and a ‘right to explanation’
B. Goodman and S. Flaxman · 2016
Earlier work this paper cites.
Generating visual explanations
L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell · 2016
Earlier work this paper cites.
Examples are not enough, learn to criticize! Criticism for interpretability
B. Kim, R. Khanna, and O. Koyejo · 2016
Earlier work this paper cites.
Interacting with predictions: Visual inspection of black-box machine learning models
J. Krause, A. Perer, and K. Ng · 2016
Earlier work this paper cites.
Rationalizing neural predictions
T. Lei, R. Barzilay, and T. Jaakkola · 2016
Earlier work this paper cites.
A. Nguyen, J. Yosinski, and J. Clune · 2016
Earlier work this paper cites.
“Why should I trust you?” Explaining the predictions of any classifier
M. Ribeiro, S. Singh, and C. Guestrin · 2016
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2016
Earlier work this paper cites.
Learning sparse two-level Boolean rules
G. Su, D. Wei, K. R. Varshney, and D. M. Malioutov · 2016
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Towards a rigorous science of interpretable machine learning
F. Doshi-Velez and B. Kim · 2017
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P. W. Koh and P. Liang · 2017
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Unified framework for interpretable methods
S. Lundberg and S.-I. Lee · 2017
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Meaningful information and the right to explanation
A. D. Selbst and J. Powles · 2017
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Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation
S. Wachter, B. Mittelstadt, and L. Floridi · 2017
The future of artificial intelligence depends on trust, 2018
A. Rao and E. Cameron · 2018
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Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
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Intelligible artificial intelligence
D. S. Weld and G. Bansal · 2018
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Interpretable convolutional neural networks
Q. Zhang, Y. N. Wu, and S.-C. Zhu · 2018
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Machine learning interpretability: A survey on methods and metrics
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR, 2017
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A Bayesian framework for learning rule sets for interpretable classification
T. Wang, C. Rudin, F. Doshi-Velez, Y. Liu, E. Klampfl, and P. MacNeille · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
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Boolean decision rules via column generation
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Explanations based on the missing: Towards contrastive explanations with pertinent negatives
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