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Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases.
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Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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A survey on feature selection methods
Chandrashekar, G. and Sahin, F · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
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Wang, F. and Rudin, C · 2015
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Examples are not enough, learn to criticize! criticism for interpretability
Kim, B., Koyejo, O., Khanna, R., et al · 2016
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Interpretable decision sets: A joint framework for description and prediction
Lakkaraju, H., Bach, S. H., and Leskovec, J · 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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Towards a rigorous science of interpretable machine learning, 2017
Doshi-Velez, F. and Kim, B · 2017
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Distilling a neural network into a soft decision tree
Frosst, N. and Hinton, G · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
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On the robustness of interpretability methods
Alvarez-Melis, D. and Jaakkola, T. S · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Öztireli, C., and Gross, M · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
Issues with post-hoc counterfactual explanations: a discussion
Laugel, T., Lesot, M.-J., Marsala, C., and Detyniecki, M · 2019
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A multidisciplinary survey and framework for design and evaluation of explainable ai systems. arxiv
Mohseni, S., Zarei, N., and Ragan, E · 2019
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Interpretable machine learning: definitions, methods, and applications
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
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Actionable recourse in linear classification
Ustun, B., Spangher, A., and Liu, Y · 2019
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Chen, J., Song, L., Wainwright, M., and Jordan, M · 2018
Cited alongside, same era.
Explaining explanations: An overview of interpretability of machine learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L · 2018
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A survey of methods for explaining black box models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D · 2018
Cited alongside, same era.
The mythos of model interpretability
Lipton, Z. C · 2018
Cited alongside, same era.
Model agnostic supervised local explanations
Plumb, G., Molitor, D., and Talwalkar, A. S · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Ribeiro, M. T., Singh, S., and Guestrin, C · 2018
Cited alongside, same era.
Representer point selection for explaining deep neural networks
Yeh, C.-K., Kim, J., Yen, I. E.-H., and Ravikumar, P. K · 2018
Cited alongside, same era.
Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Bansal, G., Wu, T., Zhu, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., and Weld, D. S · 2020
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The hidden assumptions behind counterfactual explanations and principal reasons
Barocas, S., Selbst, A. D., and Raghavan, M · 2020
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Explainable machine learning in deployment
Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., and Eckersley, P · 2020
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Human factors in model interpretability: Industry practices, challenges, and needs
Hong, S. R., Hullman, J., and Bertini, E · 2020
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Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., and Wortman Vaughan, J · 2020
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Learning model-agnostic counterfactual explanations for tabular data
Pawelczyk, M., Broelemann, K., and Kasneci, G · 2020
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Explaining groups of points in low-dimensional representations
Plumb, G., Terhorst, J., Sankararaman, S., and Talwalkar, A · 2020
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Face: feasible and actionable counterfactual explanations
Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., and Flach, P · 2020
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Beyond individualized recourse: Interpretable and interactive summaries of actionable recourses
Rawal, K. and Lakkaraju, H · 2020
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Efficient nonparametric statistical inference on population feature importance using shapley values
Williamson, B. and Feng, J · 2020
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A learning theoretic perspective on local explainability
Li, J., Nagarajan, V., Plumb, G., and Talwalkar, A · 2021
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