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The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions.
The multi-armed bandit problem: decomposition and computation
M. N. Katehakis and A. F. Veinott Jr · 1987
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Generalized additive models
T. J. Hastie and R. J. Tibshirani · 1990
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Extracting tree-structured representations of trained networks
M. Craven and J. W. Shavlik · 1996
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Making sense of a forest of trees
H. Chipman, E. George, and R. McCulloh · 1998
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Knowledge discovery via multiple models
P. Domingos · 1998
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A simple, fast, and effective rule learner
W. W. Cohen and Y. Singer · 1999
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Lightweight rule induction
S. M. Weiss and N. Indurkhya · 2000
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A simple generalisation of the area under the roc curve for multiple class classification problems
D. J. Hand and R. J. Till · 2001
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Extracting decision trees from trained neural networks
O. Boz · 2002
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Comprehensible credit scoring models using rule extraction from support vector machines
D. Martens, B. Baesens, T. Van Gestel, and J. Vanthienen · 2007
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Maximum likelihood rule ensembles
K. Dembczyński, W. Kotłowski, and R. Słowiński · 2008
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Predictive learning via rule ensembles
J. Friedman and B. E. Popescu · 2008
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Explaining classifications for individual instances
M. Robnik-Šikonja and I. Kononenko · 2008
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Prototype selection for interpretable classification
J. Bien and R. Tibshirani · 2011
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
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Comprehensible classification models: a position paper
A. A. Freitas · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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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
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Inferring team task plans from human meetings: A generative modeling approach with logic-based prior
B. Kim, C. M. Chacha, and J. A. Shah · 2015
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
B. Letham, C. Rudin, T. H. McCormick, D. Madigan, et al · 2015
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The black box society: The secret algorithms that control money and information
F. Pasquale · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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Visualizing the effects of predictor variables in black box supervised learning models
D. W. Apley and J. Zhu · 2016
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Long short-term memory-networks for machine reading
J. Cheng, L. Dong, and M. Lapata · 2016
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A framework for considering comprehensibility in modeling
M. Gleicher · 2016
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Eu regulations on algorithmic decision-making and a “right to explanation”
B. Goodman and S. Flaxman · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
B. Kim, R. Khanna, and O. Koyejo · 2016
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Interpretable decision sets: A joint framework for description and prediction
H. Lakkaraju, S. H. Bach, and J. Leskovec · 2016
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Understanding neural networks through representation erasure
J. Li, W. Monroe, and D. Jurafsky · 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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Automatic neural reconstruction from petavoxel of electron microscopy data
A. Suissa-Peleg, D. Haehn, S. Knowles-Barley, V. Kaynig, T. R. Jones, A. Wilson, R. Schalek, J. W. Lichtman, and H. Pfister · 2016
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Axis: Generating explanations at scale with learnersourcing and machine learning
J. J. Williams, J. Kim, A. Rafferty, S. Maldonado, K. Z. Gajos, W. S. Lasecki, and N. Heffernan · 2016
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Interpreting models via single tree approximation
Y. Zhou and G. Hooker · 2016
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Quint: Interpretable question answering over knowledge bases
A. Abujabal, R. S. Roy, M. Yahya, and G. Weikum · 2017
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Explaining recurrent neural network predictions in sentiment analysis
L. Arras, G. Montavon, K.-R. Müller, and W. Samek · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
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Regulating algorithms’ regulation? first ethico-legal principles, problems, and opportunities of algorithms
G. Comandè · 2017
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Towards a rigorous science of interpretable machine learning
F. Doshi-Velez and B. Kim · 2017
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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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
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
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Joint concept learning and semantic parsing from natural language explanations
S. Srivastava, I. Labutov, and T. Mitchell · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Why a right to explanation of automated decision-making does not exist in the general data protection regulation
S. Wachter, B. Mittelstadt, and L. Floridi · 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.
Scalable bayesian rule lists
H. Yang, C. Rudin, and M. Seltzer · 2017
Cited alongside, same era.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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Lionets: local interpretation of neural networks through penultimate layer decoding
I. Mollas, N. Bassiliades, and G. Tsoumakas · 2019
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Definitions, methods, and applications in interpretable machine learning
W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu · 2019
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Interpretml: A unified framework for machine learning interpretability
H. Nori, S. Jenkins, P. Koch, and R. Caruana · 2019
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Investigating robustness and interpretability of link prediction via adversarial modifications
P. Pezeshkpour, Y. Tian, and S. Singh · 2019
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A. Adadi and M. Berrada · 2018
Cited alongside, same era.
Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
Cited alongside, same era.
Towards robust interpretability with self-explaining neural networks
D. Alvarez Melis and T. Jaakkola · 2018
Cited alongside, same era.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2018
Cited alongside, same era.
Learning to explain: An information-theoretic perspective on model interpretation
J. Chen, L. Song, M. Wainwright, and M. Jordan · 2018
Cited alongside, same era.
Boolean decision rules via column generation
S. Dash, O. Gunluk, and D. Wei · 2018
Cited alongside, same era.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
A. Dhurandhar, P.-Y. Chen, R. Luss, C.-C. Tu, P. Ting, K. Shanmugam, and P. Das · 2018
Cited alongside, same era.
N. F. Rajani, B. McCann, C. Xiong, and R. Socher · 2019
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Explainable AI: interpreting, explaining and visualizing deep learning
W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K.-R. Müller · 2019
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Global explanations with local scoring
M. Setzu, R. Guidotti, A. Monreale, and F. Turini · 2019
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Alime: Autoencoder based approach for local interpretability
S. M. Shankaranarayana and D. Runje · 2019
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A survey on explainable artificial intelligence (xai): towards medical xai
E. Tjoa and C. Guan · 2019
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Interpretable counterfactual explanations guided by prototypes
A. Van Looveren and J. Klaise · 2019
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Bim: Towards quantitative evaluation of interpretability methods with ground truth
M. Yang and B. Kim · 2019
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M. R. Zafar and N. M. Khan · 2019
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Debugging tests for model explanations
J. Adebayo, M. Muelly, I. Liccardi, and B. Kim · 2020
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Neural additive models: Interpretable machine learning with neural nets
R. Agarwal, N. Frosst, X. Zhang, R. Caruana, and G. E. Hinton · 2020
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Relation-based counterfactual explanations for bayesian network classifiers
E. Albini, A. Rago, P. Baroni, and F. Toni · 2020
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Py-ciu: A python library for explaining machine learning predictions using contextual importance and utility
S. Anjomshoae, T. Kampik, and K. Främling · 2020
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al · 2020
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Ceml: Counterfactuals for explaining machine learning models - a python toolbox
A. Artelt · 2020
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Explanatory model analysis, 2020
P. Biecek and T. Burzykowski · 2020
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Machine learning explainability via microaggregation and shallow decision trees
A. Blanco-Justicia, J. Domingo-Ferrer, S. Martínez, and D. Sánchez · 2020
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Qlime-a quadratic local interpretable model-agnostic explanation approach
S. Bramhall, H. Horn, M. Tieu, and N. Lohia · 2020
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If and or: Real and counterfactual possibilities in their truth and probability
R. M. Byrne and P. Johnson-Laird · 2020
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Natural language processing
K. Chowdhary · 2020
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A survey of the state of explainable ai for natural language processing
M. Danilevsky, K. Qian, R. Aharonov, Y. Katsis, B. Kawas, and P. Sen · 2020
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Evaluating local explanation methods on ground truth
R. Guidotti · 2020
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Explaining image classifiers generating exemplars and counter-exemplars from latent representations
R. Guidotti, A. Monreale, S. Matwin, and D. Pedreschi · 2020
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Explaining any time series classifier
R. Guidotti, A. Monreale, F. Spinnato, D. Pedreschi, and F. Giannotti · 2020
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Evaluating explainable ai: Which algorithmic explanations help users predict model behavior?
P. Hase and M. Bansal · 2020
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How can i explain this to you? an empirical study of deep neural network explanation methods
J. V. Jeyakumar, J. Noor, Y.-H. Cheng, L. Garcia, and M. Srivastava · 2020
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Dace: Distribution-aware counterfactual explanation by mixed-integer linear optimization
K. Kanamori et al · 2020
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Model-agnostic counterfactual explanations for consequential decisions
A.-H. Karimi et al · 2020
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Lessons from the pulse model and discussion. the gradient, 2020
A. Kurenkov · 2020
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Explaining sentiment classification with synthetic exemplars and counter-exemplars
O. Lampridis, R. Guidotti, and S. Ruggieri · 2020
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Interpretable Machine Learning
C. Molnar · 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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Doctor xai: an ontology-based approach to black-box sequential data classification explanations
C. Panigutti, A. Perotti, and D. Pedreschi · 2020
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Face: feasible and actionable counterfactual explanations
R. Poyiadzi, K. Sokol, R. Santos-Rodriguez, T. De Bie, and P. Flach · 2020
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A modified perturbed sampling method for local interpretable model-agnostic explanation
S. Shi, X. Zhang, and W. Fan · 2020
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Tree space prototypes: Another look at making tree ensembles interpretable
S. Tan, M. Soloviev, G. Hooker, and M. T. Wells · 2020
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Counterfactual explanations for machine learning: A review
S. Verma, J. Dickerson, and K. Hines · 2020
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Context-guided bert for targeted aspect-based sentiment analysis
Z. Wu and D. C. Ong · 2020
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On completeness-aware concept-based explanations in deep neural networks
C.-K. Yeh, B. Kim, S. Arik, C.-L. Li, T. Pfister, and P. Ravikumar · 2020
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