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With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable.
Exploratory data analysis
J. W. Tukey · 1977
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Classification and regression trees
L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen · 1984
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Using the adap learning algorithm to forecast the onset of diabetes mellitus
J. W. Smith, J. Everhart, W. Dickson, W. Knowler, and R. Johannes · 1988
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Statlog (german credit data) data set
H. Hofmann · 1994
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The table lens: merging graphical and symbolic representations in an interactive focus+ context visualization for tabular information
R. Rao and S. K. Card · 1994
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Improved heterogeneous distance functions
D. R. Wilson and T. R. Martinez · 1997
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Counterfactuals
D. Lewis · 2013
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Explaining explanation
D.-H. Ruben · 2015
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Interacting with predictions: Visual inspection of black-box machine learning models
J. Krause, A. Perer, and K. Ng · 2016
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“Why should I trust you?”: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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A workflow for visual diagnostics of binary classifiers using instance-level explanations
J. Krause, A. Dasgupta, J. Swartz, Y. Aphinyanaphongs, and E. Bertini · 2017
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Towards better analysis of deep convolutional neural networks
M. Liu, J. Shi, Z. Li, C. Li, J. Zhu, and S. Liu · 2017
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Towards better analysis of machine learning models: A visual analytics perspective
S. Liu, X. Wang, M. Liu, and J. Zhu · 2017
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Understanding hidden memories of recurrent neural networks
Y. Ming, S. Cao, R. Zhang, Z. Li, Y. Chen, Y. Song, and H. Qu · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
S. Wachter, B. Mittelstadt, and C. Russell · 2017
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Do convolutional neural networks learn class hierarchy?
B. Alsallakh, A. Jourabloo, M. Ye, X. Liu, and L. Ren · 2018
Cited alongside, same era.
’it’s reducing a human being to a percentage’ perceptions of justice in algorithmic decisions
R. Binns, M. Van Kleek, M. Veale, U. Lyngs, J. Zhao, and N. Shadbolt · 2018
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Visual analytics in deep learning: An interrogative survey for the next frontiers
F. Hohman, M. Kahng, R. Pienta, and D. H. Chau · 2018
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Activis: Visual exploration of industry-scale deep neural network models
M. Kahng, P. Y. Andrews, A. Kalro, and D. H. P. Chau · 2018
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Analyzing the training processes of deep generative models
M. Liu, J. Shi, K. Cao, J. Zhu, and S. Liu · 2018
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LSTMVis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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RuleMatrix: Visualizing and understanding classifiers with rules
Y. Ming, H. Qu, and E. Bertin · 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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Efficient search for diverse coherent explanations
C. Russell · 2019
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Explanation by progressive exaggeration
S. Singla, B. Pollack, J. Chen, and K. Batmanghelich · 2019
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Seq2seq-vis: A visual debugging tool for sequence-to-sequence models
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H. Strobelt, S. Gehrmann, H. Pfister, and A. M. Rush · 2018
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Ganviz: A visual analytics approach to understand the adversarial game
J. Wang, L. Gou, H. Yang, and H. Shen · 2018
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Synthesizing tabular data using generative adversarial networks
L. Xu and K. Veeramachaneni · 2018
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A comparison of regression models for prediction of graduate admissions
M. S. Acharya, A. Armaan, and A. S. Antony · 2019
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Techniques for interpretable machine learning
M. Du, N. Liu, and X. Hu · 2019
Cited alongside, same era.
Gamut: A design probe to understand how data scientists understand machine learning models
F. Hohman, A. Head, R. Caruana, R. DeLine, and S. M. Drucker · 2019
Cited alongside, same era.
Model-agnostic counterfactual explanations for consequential decisions
A.-H. Karimi, G. Barthe, B. Belle, and I. Valera · 2019
Cited alongside, same era.
H. Strobelt, S. Gehrmann, M. Behrisch, A. Perer, H. Pfister, and A. M. Rush · 2019
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Actionable recourse in linear classification
B. Ustun, A. Spangher, and Y. Liu · 2019
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Dqnviz: A visual analytics approach to understand deep q-networks
J. Wang, L. Gou, H. Shen, and H. Yang · 2019
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The what-if tool: Interactive probing of machine learning models
J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viégas, and J. Wilson · 2019
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Fairsight: Visual analytics for fairness in decision making
Y. Ahn and Y. Lin · 2020
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Prince: Provider-side interpretability with counterfactual explanations in recommender systems
A. Ghazimatin, O. Balalau, R. Saha Roy, and G. Weikum · 2020
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Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations
F. Hohman, H. Park, C. Robinson, and D. H. Chau · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
R. K. Mothilal, A. Sharma, and C. Tan · 2020
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explainer: A visual analytics framework for interactive and explainable machine learning
T. Spinner, U. Schlegel, H. Schäfer, and M. El-Assady · 2020
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