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Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes.
Rationale-augmented convolutional neural networks for text classification
Zhang, Y., Marshall, I. J., and Wallace, B. C · 1906
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Fast effective rule induction
Cohen, W. W · 1995
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Explanation-augmented svm: an approach to incorporating domain knowledge into svm learning
Sun, Q. and DeJong, G · 2005
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Modeling annotators: A generative approach to learning from annotator rationales
Zaidan, O. F. and Eisner, J · 2008
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Automatically generating annotator rationales to improve sentiment classification
Ainur, Y., Choi, Y., and Cardie, C · 2010
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Annotator rationales for visual recognition
Donahue, J. and Grauman, K · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Supervised patient similarity measure of heterogeneous patient records
Sun, J., Wang, F., Hu, J., and Edabollahi, S · 2012
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Deep learning for content-based image retrieval: A comprehensive study
Wan, J., Wang, D., Hoi, S., Wu, P., Zhu, J., Zhang, Y., and Li, J · 2014
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Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N · 2015
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Overview of the visceral retrieval benchmark 2015
Jimenez-del-Toro, O., Hanbury, A., Langs, G., Foncubierta–Rodriguez, A., and Muller, H · 2015
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EU regulations on algorithmic decision-making and a ‘right to explanation’
Goodman, B. and Flaxman, S · 2016
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Generating visual explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., and Darrell, T · 2016
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Photo aesthetics ranking network with attributes and content adaptation
Kong, S., Shen, X., Lin, Z., Mech, R., and Fowlkes, C · 2016
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Rationalizing neural predictions
Lei, T., Barzilay, R., and Jaakkola, T · 2016
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Why is that relevant? collecting annotator rationales for relevance judgments
McDonnell, T., Lease, M., Kutlu, M., and Elsayed, T · 2016
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Joint learning of semantic and latent attributes
Peng, P., Tian, Y., Xiang, T., Wang, Y., and Huang, T · 2016
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“Why should I trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Explanation and justification in machine learning: A survey
Biran, O. and Cotton, C · 2017
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Explanation in artificial intelligence: Insights from the social sciences
Miller, T · 2017
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Explainable AI: Beware of inmates running the asylum or: How I learnt to stop worrying and love the social and behavioural sciences
Miller, T., Howe, P., and Sonenberg, L · 2017
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Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., and Müller, K.-R · 2017
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Meaningful information and the right to explanation
Selbst, A. D. and Powles, J · 2017
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Why a right to explanation of automated decision-making does not exist in the general data protection regulation
Wachter, S., Mittelstadt, B., and Floridi, L · 2017
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Breiman, L · 2017
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A formal framework to characterize interpretability of procedures
Dhurandhar, A., Iyengar, V., Luss, R., and Shanmugam, K · 2017
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Towards a rigorous science of interpretable machine learning, 2017
Doshi-Velez, F. and Kim, B · 2017
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Accountability of ai under the law: The role of explanation, 2017
Doshi-Velez, F., Mason Kortz, R. B., Bavitz, C., Sam Gershman, D. O., Schieber, S., Waldo, J., Weinberger, D., and Wood, A · 2017
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Predicting human olfactory perception from chemical features of odor molecules
Keller, A., Gerkin, R. C., Guan, Y., Dhurandhar, A., Turu, G., Szalai, B., Mainland, J. D., Ihara, Y., Yu, C. W., Wolfinger, R., Vens, C., Schietgat, L., De Grave, K., Norel, R., Stolovitzky, G., Cecchi, G. A., Vosshall, L. B., and Meyer, P · 2017
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A unified approach to interpreting model predictions
Lundberg, S. and Lee, S.-I · 2017
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A bayesian framework for learning rule sets for interpretable classification
Wang, T., Rudin, C., Doshi-Velez, F., Liu, Y., Klampfl, E., and MacNeille, P · 2017
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Interpreting blackbox models via model extraction
Bastani, O., Kim, C., and Bastani, H · 2018
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (isic)
Codella, N. C., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al · 2018
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Codella, N. C., Lin, C.-C., Halpern, A., Hind, M., Feris, R., and Smith, J. R · 2018
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Boolean decision rules via column generation
Dash, S., Gunluk, O., and Wei, D · 2018
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Large-scale retrieval for medical image analytics: A comprehensive review
Li, Z., Zhang, X., Muller, H., and Zhang, S · 2018
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TED: Teaching AI to explain its decisions
Hind, M., Wei, D., Campbell, M., Codella, N. C. F., Dhurandhar, A., Mojsilovic, A., Ramamurthy, K. N., and Varshney, K. R · 2019
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