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The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general.
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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
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The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops . IEEE, 94–101
Patrick Lucey, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews. 2010 · 2010
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An Efficient Explanation of Individual Classifications using Game Theory
Erik Strumbelj and Igor Kononenko. 2010 · 2010
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Robust statistics: the approach based on influence functions . Vol. 196
Frank R Hampel, Elvezio M Ronchetti, Peter J Rousseeuw, and Werner A Stahel. 2011 · 2011
Earlier work this paper cites.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
Earlier work this paper cites.
On the interpretation of weight vectors of linear models in multivariate neuroimaging
Stefan Haufe, Frank Meinecke, Kai Görgen, Sven Dähne, John-Dylan Haynes, Benjamin Blankertz, and Felix Bießmann. 2014 · 2014
Earlier work this paper cites.
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. 2014 · 2014
Earlier work this paper cites.
Visualizing and Understanding Convolutional Networks. In ECCV . 818–833
Matthew D. Zeiler and Rob Fergus. 2014 · 2014
Cited alongside, same era.
jsPsych: A JavaScript library for creating behavioral experiments in a Web browser
Joshua R. de Leeuw. 2015 · 2015
Cited alongside, same era.
Algorithm aversion: People erroneously avoid algorithms after seeing them err
Berkeley J. Dietvorst, Joseph P. Simmons, and Cade Massey. 2015 · 2015
Cited alongside, same era.
Interactive and interpretable machine learning models for human machine collaboration
Been Kim. 2015 · 2015
Cited alongside, same era.
Algorithmic Bias. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16 . ACM Press, New York, New York, USA, 2125–2126
Sara Hajian, Francesco Bonchi, and Carlos Castillo. 2016 · 2016
Cited alongside, same era.
The Promise and Peril of Human Evaluation for Model Interpretability
Bernease Herman. 2017 · 2017
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Understanding Black-box Predictions via Influence Functions. In ICML , Doina Precup and Yee Whye Teh (Eds.), Vol. 70. 1885–1894
Pang Wei Koh and Percy Liang. 2017 · 2017
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Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
Later among the works it cites.
The Doctor Just Won’t Accept That!
Zachary C Lipton. 2017 · 2017
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A Unified Approach to Interpreting Model Predictions. In NIPS . 4768–4777
Scott M. Lundberg and Su-In Lee. 2017 · 2017
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Interpretable decision sets: A joint framework for description and prediction. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . ACM, 1675–1684
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec. 2016 · 2016
Cited alongside, same era.
The mythos of model interpretability
Zachary C Lipton. 2016 · 2016
Cited alongside, same era.
Explaining nonlinear classification decisions with deep Taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller. 2017 · 2016
Cited alongside, same era.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier. In SIGKDD . 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Evaluating the Visualization of What a Deep Neural Network Has Learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2017 · 2016
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
https://github.com/thoughtworksarts/EmoPy
EmoPy. [n. d.]
Cited in the paper.
Later among the works it cites.
Explanation in artificial intelligence: insights from the social sciences
Tim Miller. 2017 · 2017
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Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans. 2018 · 2018
Later among the works it cites.
A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
Later among the works it cites.
Anchors: High-precision model-agnostic explanations. In AAAI Conference on Artificial Intelligence
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Later among the works it cites.
Quantifying Interpretability and Trust in Machine Learning Systems
Philipp Schmidt and Felix Biessmann. 2019 · 2019
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