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We study the faithfulness of an explanation system to the underlying prediction model.
A value for n-person games
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
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Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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A data-driven approach to predict the success of bank telemarketing
Sérgio Moro, Paulo Cortez, and Paulo Rita · 2014
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Why should I trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S Jaakkola · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan · 2018
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Reliable writer identification in medieval manuscripts through page layout features: The “avila” bible case
Claudio De Stefano, Marilena Maniaci, Francesco Fontanella, and A Scotto di Freca · 2018
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The mythos of model interpretability
Zachary C Lipton · 2018
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A human-grounded evaluation benchmark for local explanations of machine learning
Sina Mohseni, Jeremy E Block, and Eric D Ragan · 2018
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A new method to compare the interpretability of rule-based algorithms
Vincent Margot and George Luta · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Towards probabilistic sufficient explanations
Eric Wang, Pasha Khosravi, and Guy Van den Broeck · 2020
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Gradient-based analysis of nlp models is manipulable
Junlin Wang, Jens Tuyls, Eric Wallace, and Sameer Singh · 2020
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Towards the unification and robustness of perturbation and gradient based explanations
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, and Himabindu Lakkaraju · 2021
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A theoretical explanation for perplexing behaviors of backpropagation-based visualizations
Weili Nie, Yang Zhang, and Ankit Patel · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Invase: Instance-wise variable selection using neural networks
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2018
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Constraints-based explanations of classifications
Daniel Deutch and Nave Frost · 2019
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Optimal sparse decision trees
Xiyang Hu, Cynthia Rudin, and Margo Seltzer · 2019
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Interpretable Machine Learning
Christoph Molnar · 2019
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Learning optimized risk scores
Berk Ustun and Cynthia Rudin · 2019
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To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
Elvio Amparore, Alan Perotti, and Paolo Bajardi · 2021
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On the objective evaluation of post hoc explainers
Zachariah Carmichael and Walter J Scheirer · 2021
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Aligning faithful interpretations with their social attribution
Alon Jacovi and Yoav Goldberg · 2021
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How can i choose an explainer? an application-grounded evaluation of post-hoc explanations
Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, and João Gama · 2021
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Machine learning techniques for accountability
Been Kim and Finale Doshi-Velez · 2021
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Connecting interpretability and robustness in decision trees through separation
Michal Moshkovitz, Yao-Yuan Yang, and Kamalika Chaudhuri · 2021
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Revisiting the evaluation of class activation mapping for explainability: A novel metric and experimental analysis
Samuele Poppi, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara · 2021
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Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach · 2021
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Counterfactual explanations can be manipulated
Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh · 2021
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Developing a fidelity evaluation approach for interpretable machine learning
Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira, and Renuka Sindhgatta · 2021
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Evaluating the quality of machine learning explanations: A survey on methods and metrics
Jianlong Zhou, Amir H Gandomi, Fang Chen, and Andreas Holzinger · 2021
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