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The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this information.
One explanation does not fit all: A toolkit and taxonomy of AI explainability techniques, 2019
Arya, V., Bellamy, R. K. E., Chen, P.-Y., Dhurandhar, A., Hind, M., Hoffman, S. C., Houde, S., Liao, Q. V., Luss, R., Mojsilović, A., Mourad, S., Pedemonte, P., Raghavendra, R., Richards, J., Sattigeri, P., Shanmugam, K., Singh, M., Varshney, K. R., Wei, D., and Zhang, Y · 1909
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Q-learning
Watkins, C. J. and Dayan, P · 1992
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Explainable AI for designers: A human-centered perspective on mixed-initiative co-creation
Zhu, J., Liapis, A., Risi, S., Bidarra, R., and Youngblood, G. M · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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The MOOClet framework: Improving online education through experimentation and personalization of modules
Williams, J., Li, N., Kim, J., Whitehill, J., Maldonado, S., Pechenizkiy, M., Chu, L., and Heffernan, N · 2014
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Layer-wise relevance propagation for neural networks with local renormalization layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W · 2016
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Examples are not enough, learn to criticize! Criticism for interpretability
Kim, B., Khanna, R., and Koyejo, O. O · 2016
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The mythos of model interpretability
Lipton, Z. C · 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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What does explainable AI really mean? A new conceptualization of perspectives
Doran, D., Schulz, S., and Besold, T. R · 2017
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
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Protodash: Fast interpretable prototype selection
Gurumoorthy, K. S., Dhurandhar, A., and Cecchi, G · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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Explainable AI: Beware of inmates running the asylum
Miller, T., Howe, P., and Sonenberg, L · 2017
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Metrics for explainable AI: Challenges and prospects
Hoffman, R. R., Mueller, S. T., Klein, G., and Litman, J · 2018
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Human-in-the-loop interpretability prior
Lage, I., Ross, A., Gershman, S. J., Kim, B., and Doshi-Velez, F · 2018
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What is interpretable? Using machine learning to design interpretable decision-support systems
Lahav, O., Mastronarde, N., and van der Schaar, M · 2018
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Towards a grounded dialog model for explainable artificial intelligence
Madumal, P., Miller, T., Vetere, F., and Sonenberg, L · 2018
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Manipulating and measuring model interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Vaughan, J. W., and Wallach, H · 2018
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Explaining nonlinear classification decisions with deep Taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R · 2017
Cited alongside, same era.
Smoothgrad: Removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
Adadi, A. and Berrada, M · 2018
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Deep learning for classical Japanese literature
Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., and Ha, D · 2018
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Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Dhurandhar, A., Chen, P.-Y., Luss, R., Tu, C.-C., Ting, P., Shanmugam, K., and Das, P · 2018
Cited alongside, same era.
Explaining explanations: An approach to evaluating interpretability of machine learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L · 2018
Cited alongside, same era.
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Anchors: High-precision model-agnostic explanations
Ribeiro, M. T., Singh, S., and Guestrin, C · 2018
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iNNvestigate neural networks!
Alber, M., Lapuschkin, S., Seegerer, P., Hägele, M., Schütt, K. T., Montavon, G., Samek, W., Müller, K.-R., Dähne, S., and Kindermans, P.-J · 2019
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Explainable reinforcement learning through a causal lens
Madumal, P., Miller, T., Sonenberg, L., and Vetere, F · 2019
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Kannada-MNIST: A new handwritten digits dataset for the Kannada language
Prabhu, V. U · 2019
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Interpretable counterfactual explanations guided by prototypes
Van Looveren, A. and Klaise, J · 2019
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Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
Hase, P. and Bansal, M · 2020
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