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In recent years, the community of 'explainable artificial intelligence' (XAI) has created a vast body of methods to bridge a perceived gap between model 'complexity' and 'interpretability'.
A Value for n-Person Games
Shapley, L. S · 1953
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The paramorphic representation of clinical judgment
Hoffman, P. J · 1960
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A Revised Definition for Suppressor Variables: A Guide To Their Identification and Interpretation , A Revised Definition for Suppressor Variables: A Guide To Their Identification and Interpretation
Conger, A. J · 1974
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Random Forests
Breiman, L · 2001
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Greedy Function Approximation: A Gradient Boosting Machine
Friedman, J. H · 2001
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Analysis of regression in game theory approach
Lipovetsky, S. and Conklin, M · 2001
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Review and comparison of methods to study the contribution of variables in artificial neural network models
Gevrey, M., Dimopoulos, I., and Lek, S · 2003
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Paths and consistency in additive cost sharing
Friedman, E. J · 2004
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Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., and Müller, K.-R · 2004
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Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression
Friedman, L. and Wall, M · 2005
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The Feature Importance Ranking Measure
Zien, A., Krämer, N., Sonnenburg, S., and Rätsch, G · 2009
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How to explain individual classification decisions
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Müller, K.-R · 2010
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On the interpretation of weight vectors of linear models in multivariate neuroimaging
Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.-D., Blankertz, B., and Bießmann, F · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Visualizing and Understanding Convolutional Networks
Zeiler, M. D. and Fergus, R · 2014
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Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E. and Kononenko, I · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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Striving for simplicity: The all convolutional net
Springenberg, J., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
Cited alongside, same era.
Layer-Wise Relevance Propagation for Deep Neural Network Architectures
Binder, A., Bach, S., Montavon, G., Müller, K.-R., and Samek, W · 2016
Cited alongside, same era.
” Why should I trust you?” Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Cited alongside, same era.
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F. and Kim, B · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Benchmarking attribution methods with relative feature importance
Yang, M. and Kim, B · 2019
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Visualizing the effects of predictor variables in black box supervised learning models
Apley, D. W. and Zhu, J · 2020
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., and Herrera, F · 2020
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Explaining the explainer: A first theoretical analysis of lime
Garreau, D. and von Luxburg, U · 2020
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Model-agnostic effects plots for interpreting machine learning models
Grömping, U · 2020
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Montavon, G., Bach, S., Binder, A., Samek, W., and Müller, K.-R · 2017
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Müller, K · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Cited alongside, same era.
Axiomatic Attribution for Deep Networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Wachter, S., Mittelstadt, B., and Russell, C · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
Cited alongside, same era.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Jacovi, A. and Goldberg, Y · 2020
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Feature relevance quantification in explainable AI: A causal problem
Janzing, D., Minorics, L., and Bloebaum, P · 2020
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Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., and Schneider, G · 2020
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Towards best practice in explaining neural network decisions with lrp
Kohlbrenner, M., Bauer, A., Nakajima, S., Binder, A., Samek, W., and Lapuschkin, S · 2020
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Interpretable Machine Learning
Molnar, C · 2020
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When explanations lie: Why many modified BP attributions fail
Sixt, L., Granz, M., and Landgraf, T · 2020
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Explaining individual predictions when features are dependent: More accurate approximations to shapley values
Aas, K., Jullum, M., and Løland, A · 2021
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Deep learning in cancer diagnosis, prognosis and treatment selection
Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., and Waddell, N · 2021
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OpenXAI: Towards a transparent evaluation of model explanations
Agarwal, C., Krishna, S., Saxena, E., Pawelczyk, M., Johnson, N., Puri, I., Zitnik, M., and Lakkaraju, H · 2022
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A rigorous study of the deep taylor decomposition
Sixt, L. and Landgraf, T · 2022
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Scrutinizing xai using linear ground-truth data with suppressor variables
Wilming, R., Budding, C., Müller, K.-R., and Haufe, S · 2022
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