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Explainable Artificial Intelligence (XAI)has received a great deal of attention recently.
On the (In)fidelity and Sensitivity for Explanations
C.-K. Yeh, C.-Y. Hsieh, A. Sai Suggala, D. Inouye, and P. Ravikumar · 1903
Earlier work this paper cites.
Weighted voting doesn”t work: a mathematical analysis
J. C. Banzhaf · 1965
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Understanding waterfall plots
T. W. Gillespie · 2012
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
Openml: Networked science in machine learning
J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo · 2013
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
Earlier work this paper cites.
Adverse events in robotic surgery: A retrospective study of 14 years of fda data
H. Alemzadeh, J. Raman, N. Leveson, Z. Kalbarczyk, and R. K. Iyer · 2016
Earlier work this paper cites.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
A. Datta, S. Sen, and Y. Zick · 2016
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
C. O’Neil · 2016
Earlier work this paper cites.
Why Should I Trust You?": Explaining the Predictions of Any Classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Earlier work this paper cites.
A causal framework for explaining the predictions of black-box sequence-to-sequence models
D. Alvarez-Melis and T. Jaakkola · 2017
Earlier work this paper cites.
OpenML benchmarking suites and the OpenML100
B. Bischl, G. Casalicchio, M. Feurer, F. Hutter, M. Lang, R. G. Mantovani, J. N. van Rijn, and J. Vanschoren · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Cited alongside, same era.
Learning Important Features Through Propagating Activation Differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
Cited alongside, same era.
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
S. Wachter, B. Mittelstadt, and C. Russell · 2017
Cited alongside, same era.
On the Robustness of Interpretability Methods
D. Alvarez-Melis and T. S. Jaakkola · 2018
Cited alongside, same era.
iml: An r package for interpretable machine learning
C. Molnar, B. Bischl, and G. Casalicchio · 2018
Later among the works it cites.
Explanations of Model Predictions with live and breakDown Packages
M. Staniak and P. Biecek · 2018
Later among the works it cites.
pyCeterisParibus: explaining Machine Learning models with Ceteris Paribus Profiles in Python
M. Kuzba, E. Baranowska, and P. Biecek · 2019
Closest in time.
Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view
O. Loyola-González · 2019
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How bad is Sacramento’s air, exactly? Google results appear at odds with reality, some say
M. McGough · 2019
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Interpretable Machine Learning
C. Molnar · 2019
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DALEX: Explainers for Complex Predictive Models in R
P. Biecek · 2018
Cited alongside, same era.
Enslaving the algorithm: From a “right to an explanation” to a “right to better decisions”?
L. Edwards and M. Veale · 2018
Cited alongside, same era.
An Introduction to Machine Learning Interpretability
N. Gill and P. Hall · 2018
Cited alongside, same era.
On The Stability of Interpretable Models
R. Guidotti and S. Ruggieri · 2018
Cited alongside, same era.
Understanding convolutional neural networks for text classification
A. Jacovi, O. Sar Shalom, and Y. Goldberg · 2018
Cited alongside, same era.
Consistent Individualized Feature Attribution for Tree Ensembles
S. M. Lundberg, G. G. Erion, and S.-I. Lee · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin · 2018
Cited alongside, same era.
lime: Local Interpretable Model-Agnostic Explanations , 2019
T. L. Pedersen and M. Benesty · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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LIME-based Explanations With Interpretable Inputs Based on Ceteris Paribus Profiles , 2019
M. Staniak and P. Biecek · 2019
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When a Computer Program Keeps You in Jail
R. Wexler · 2019
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“how do i fool you?”: Manipulating user trust via misleading black box explanations
H. Lakkaraju and O. Bastani · 2020
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