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Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment and user-studies.
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
Erico Tjoa and Cuntai Guan · 1907
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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang · 1909
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bLIMEy: Surrogate Prediction Explanations Beyond LIME
Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez, and Peter Flach · 1910
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Variabilità e mutabilità
Corrado Gini · 1912
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Applied nonparametric statistics
Wayne W Daniel · 1978
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Clustering by means of medoids
Leonard Kaufman and Peter J. Rousseeuw · 1987
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Philosophical and computational models of explanation
Paul Thagard · 1991
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Elements of Information Theory
Thomas M. Cover and Joy A. Thomas · 1991
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Tuning Complex Computer Codes to Data and Optimal Designs
Jeong Soo Park · 1992
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Toward transparency : new approaches and their application to financial markets
Daniel Vishwanath, Tara; Kaufmann · 2001
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Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
Riccardo Guidotti, Anna Monreale, Stan Matwin, and Dino Pedreschi · 2002
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
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Capacity limits of information processing in the brain, 6 2005
René Marois and Jason Ivanoff · 2005
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An empirical evaluation of deep architectures on problems with many factors of variation
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio · 2007
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Dissecting explanatory power
Petri Ylikoski and Jaakko Kuorikoski · 2010
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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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Character-Level Convolutional Networks for Text Classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Ethical guidelines for a superintelligence
Ernest Davis · 2015
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Explanatory Preferences Shape Learning and Inference, 10 2016
Tania Lombrozo · 2016
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The Mythos of Model Interpretability
Zachary C. Lipton · 2016
Explaining explanations: An overview of interpretability of machine learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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Towards Robust Interpretability with Self-Explaining Neural Networks
David Alvarez Melis and Tommi Jaakkola · 2018
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Anchors: High-Precision Model-Agnostic Explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Marco Ancona, Enea Ceolini, Cengiz Oztireli, and Markus Gross · 2018
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory sayres · 2018
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Cited alongside, same era.
" Why should i trust you?" Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! Criticism for Interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
Cited alongside, same era.
Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne · 2016
Cited alongside, same era.
Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
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Reliable writer identification in medieval manuscripts through page layout features: The “Avila” Bible case
C. De Stefano, M. Maniaci, F. Fontanella, and A. Scotto di Freca · 2018
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Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera · 2019
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Explanation in artificial intelligence: Insights from the social sciences, 2 2019
Tim Miller · 2019
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Interpretable Machine Learning
Christoph Molnar · 2019
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On the (In)fidelity and Sensitivity of Explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar · 2019
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This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su · 2019
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A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo V. Carvalho, Eduardo M. Pereira, and Jaime S. Cardoso · 2079
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