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Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years.
Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the mycin system
Edward H Shortliffe, Randall Davis, Stanton G Axline, Bruce G Buchanan, C Cordell Green, and Stanley N Cohen · 1975
Earlier work this paper cites.
Blah, a system which explains its reasoning
JL Weiner · 1980
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User participation in the reasoning processes of expert systems
Martha E Pollack, Julia Hirschberg, and Bonnie Webber · 1982
Earlier work this paper cites.
Reconstructive explanation: Explanation as complex problem solving
Michael R Wick and William B Thompson · 1989
Earlier work this paper cites.
Planning text for advisory dialogues
Johanna D Moore and Cecile L Paris · 1989
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A reactive approach to explanation
Johanna D Moore and William R Swartout · 1989
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Generating interactive explanations
Alison Cawsey · 1991
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A reactive approach to explanation: Taking the user’s feedback into account
Johanna D Moore and William R Swartout · 1991
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Visualizing learning and computation in artificial neural networks
Mark W Craven and Jude W Shavlik · 1992
Earlier work this paper cites.
Explanation in second generation expert systems
William R Swartout and Johanna D Moore · 1993
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Planning text for advisory dialogues: Capturing intentional and rhetorical information
Johanna D Moore and Cécile L Paris · 1993
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Second generation expert system explanation
Michael R Wick · 1993
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Planning interactive explanations
Alison Cawsey · 1993
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Explanation facilities and interactive systems
Hilary Johnson and Peter Johnson · 1993
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An evaluation of explanations of probabilistic inference
Henri J Suermondt and Gregory F Cooper · 1993
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Using sampling and queries to extract rules from trained neural networks
Mark W Craven and Jude W Shavlik · 1994
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Understanding neural networks via rule extraction
Rudy Setiono and Huan Liu · 1995
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Extracting rules from artificial neural networks with distributed representations
Sebastian Thrun · 1995
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The impact of explanation facilities on user acceptance of expert systems advice
L Richard Ye and Paul E Johnson · 1995
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Survey and critique of techniques for extracting rules from trained artificial neural networks
Robert Andrews, Joachim Diederich, and Alan B Tickle · 1995
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Extracting tree-structured representations of trained networks
Mark Craven and Jude W Shavlik · 1996
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Explaining results of neural networks by contextual importance and utility
Kary Främling · 1996
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Extraction of rules from discrete-time recurrent neural networks
Christian W Omlin and C Lee Giles · 1996
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Transforming rules and trees into comprehensible knowledge structures
Brian R. Gaines · 1996
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Defining explanation in probabilistic systems
Urszula Chajewska and Joseph Y Halpern · 1997
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Machine learning: Between accuracy and interpretability
Ivan Bratko · 1997
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Comprehensible knowledge discovery: gaining insight from data
M Pazzani · 1997
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Knowledge discovery: comprehensibility of the results
Irit Askira-Gelman · 1998
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A new approach to expert system explanations
Regina Barzilay, Daryl McCullough, Owen Rambow, Jonathan DeCristofaro, Tanya Korelsky, and Benoit Lavoie · 1998
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Symbolic rule extraction from the dimlp neural network
Guido Bologna · 1998
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Explanations from intelligent systems: Theoretical foundations and implications for practice
Shirley Gregor and Izak Benbasat · 1999
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On rule interestingness measures
Alex A Freitas · 1999
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An explainable artificial intelligence system for small-unit tactical behavior
Michael Van Lent, William Fisher, and Michael Mancuso · 1999
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Visual explanation of evidence with additive classifiers
Brett Poulin, Roman Eisner, Duane Szafron, Paul Lu, Russell Greiner, David S Wishart, Alona Fyshe, Brandon Pearcy, Cam MacDonell, and John Anvik · 1999
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Case-based explanation of non-case-based learning methods
Rich Caruana, Hooshang Kangarloo, JD Dionisio, Usha Sinha, and David Johnson · 1999
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Explaining collaborative filtering recommendations
Jonathan L Herlocker, Joseph A Konstan, and John Riedl · 2000
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Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
Yaochu Jin · 2000
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Designing fuzzy inference systems from data: An interpretability-oriented review
Serge Guillaume · 2001
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Interpretation of trained neural networks by rule extraction
Vasile Palade, Daniel-Ciprian Neagu, and Ron J Patton · 2001
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Nvis: An interactive visualization tool for neural networks
Matthew J Streeter, Matthew O Ward, and Sergio A Alvarez · 2001
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Symbolic knowledge extraction from trained neural networks: A sound approach
Arthur d’Avila Garcez, Krysia Broda, and Dov M Gabbay · 2001
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A review of explanation methods for bayesian networks
Carmen Lacave and Francisco J Díez · 2002
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Extracting decision trees from trained neural networks
Olcay Boz · 2002
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A methodology to explain neural network classification
Raphael Féraud and Fabrice Clérot · 2002
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From ensemble methods to comprehensible models
César Ferri, José Hernández-Orallo, and M José Ramírez-Quintana · 2002
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Rule extraction from support vector machines
Haydemar Núñez, Cecilio Angulo, and Andreu Català · 2002
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An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor
Vincent AWMM Aleven and Kenneth R Koedinger · 2002
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The role of trust in automation reliance
Mary T Dzindolet, Scott A Peterson, Regina A Pomranky, Linda G Pierce, and Hall P Beck · 2003
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Combining mental fit and data fit for classification rule selection
Claus Weihs and UM Sondhauss · 2003
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Extracting symbolic rules from trained neural network ensembles
Zhi-Hua Zhou, Yuan Jiang, and Shi-Fu Chen · 2003
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Medical diagnosis with c4. 5 rule preceded by artificial neural network ensemble
Zhi-Hua Zhou and Yuan Jiang · 2003
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Towards simple, easy-to-understand, yet accurate classifiers
Doina Caragea, Dianne Cook, and Vasant Honavar · 2003
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Review and comparison of methods to study the contribution of variables in artificial neural network models
Muriel Gevrey, Ioannis Dimopoulos, and Sovan Lek · 2003
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Discovering additive structure in black box functions
Giles Hooker · 2004
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Nomograms for visualization of naive bayesian classifier
Martin Možina, Janez Demšar, Michael Kattan, and Blaž Zupan · 2004
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Rule extraction from recurrent neural networks: A taxonomy and review
Henrik Jacobsson · 2005
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Explanation in case-based reasoning–perspectives and goals
Frode Sørmo, Jörg Cassens, and Agnar Aamodt · 2005
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Design recommendations to support automated explanation and tutoring
Dave Gomboc, Steve Solomon, Mark G Core, H Chad Lane, and Michael Van Lent · 2005
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Explainable artificial intelligence for training and tutoring
H Chad Lane, Mark G Core, Michael Van Lent, Steve Solomon, and Dave Gomboc · 2005
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Autotutor: An intelligent tutoring system with mixed-initiative dialogue
Arthur C Graesser, Patrick Chipman, Brian C Haynes, and Andrew Olney · 2005
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Rule extraction from linear support vector machines
Glenn Fung, Sathyakama Sandilya, and R Bharat Rao · 2005
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Nomograms for visualizing support vector machines
Aleks Jakulin, Martin Možina, Janez Demšar, Ivan Bratko, and Blaž Zupan · 2005
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Comprehensible credit scoring models using rule extraction from support vector machines
David Martens, Bart Baesens, Tony Van Gestel, and Jan Vanthienen · 2006
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Are we really discovering interesting knowledge from data
Alex A Freitas · 2006
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The structure and function of explanations
Tania Lombrozo · 2006
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Building explainable artificial intelligence systems
Mark G Core, H Chad Lane, Michael Van Lent, Dave Gomboc, Steve Solomon, and Milton Rosenberg · 2006
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Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning
Hisao Ishibuchi and Yusuke Nojima · 2006
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Visualization of support vector machines with unsupervised learning
Lutz Hamel · 2006
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Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs
Weiquan Wang and Izak Benbasat · 2007
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A survey of explanations in recommender systems
Nava Tintarev and Judith Masthoff · 2007
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Explaining classifications for individual instances
Marko Robnik-Šikonja and Igor Kononenko · 2007
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Seeing the forest through the trees: Learning a comprehensible model from an ensemble
Anneleen Van Assche and Hendrik Blockeel · 2007
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Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels
Baek Hwan Cho, Hwanjo Yu, Jongshill Lee, Young Joon Chee, In Young Kim, and Sun I Kim · 2007
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How it works: a field study of non-technical users interacting with an intelligent system
Joe Tullio, Anind K Dey, Jason Chalecki, and James Fogarty · 2007
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On the importance of comprehensible classification models for protein function prediction
Alex A Freitas, Daniela C Wieser, and Rolf Apweiler · 2008
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Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling
Shang-Ming Zhou and John Q Gan · 2008
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Designs for explaining intelligent agents
Steven R Haynes, Mark A Cohen, and Frank E Ritter · 2008
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Towards a model independent method for explaining classification for individual instances
Erik Štrumbelj and Igor Kononenko · 2008
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Explaining inferences in bayesian networks
Ghim-Eng Yap, Ah-Hwee Tan, and Hwee-Hwa Pang · 2008
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Why and why not explanations improve the intelligibility of context-aware intelligent systems
Brian Y Lim, Anind K Dey, and Daniel Avrahami · 2009
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A study into preferred explanations of virtual agent behavior
Maaike Harbers, Karel van den Bosch, and John-Jules Ch Meyer · 2009
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Understanding support vector machine classifications via a recommender system-like approach
David Barbella, Sami Benzaid, Janara M Christensen, Bret Jackson, X Victor Qin, and David R Musicant · 2009
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Explaining instance classifications with interactions of subsets of feature values
Erik Štrumbelj, Igor Kononenko, and M Robnik Šikonja · 2009
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A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability
Salvador García, Alberto Fernández, Julián Luengo, and Francisco Herrera · 2009
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Assessing demand for intelligibility in context-aware applications
Brian Y Lim and Anind K Dey · 2009
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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An efficient explanation of individual classifications using game theory
Erik Strumbelj and Igor Kononenko · 2010
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Explanation and reliability of prediction models: the case of breast cancer recurrence
Erik Štrumbelj, Zoran Bosnić, Igor Kononenko, Branko Zakotnik, and Cvetka Grašič Kuhar · 2010
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ivisclassifier: An interactive visual analytics system for classification based on supervised dimension reduction
Jaegul Choo, Hanseung Lee, Jaeyeon Kihm, and Haesun Park · 2010
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
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Understanding representations learned in deep architectures
Dumitru Erhan, Aaron Courville, and Yoshua Bengio · 2010
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Building comprehensible customer churn prediction models with advanced rule induction techniques
Wouter Verbeke, David Martens, Christophe Mues, and Bart Baesens · 2010
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Design and evaluation of explainable bdi agents
Maaike Harbers, Karel van den Bosch, and John-Jules Meyer · 2010
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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, and Bart Baesens · 2010
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Performance of classification models from a user perspective
David Martens, Jan Vanthienen, Wouter Verbeke, and Bart Baesens · 2011
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Designing and evaluating explanations for recommender systems
Nava Tintarev and Judith Masthoff · 2011
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A dialogue system specification for explanation
Douglas Walton · 2011
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Preferred explanations: Theory and generation via planning
Shirin Sohrabi, Jorge A Baier, and Sheila A McIlraith · 2011
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Opening black box data mining models using sensitivity analysis
Paulo Cortez and Mark J Embrechts · 2011
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Prototype selection for interpretable classification
Jacob Bien, Robert Tibshirani, et al · 2011
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Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis
Zhenyu Wang and Vasile Palade · 2011
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Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Maria Jose Gacto, Rafael Alcalá, and Francisco Herrera · 2011
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User-oriented assessment of classification model understandability
Hiva Allahyari and Niklas Lavesson · 2011
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Why-oriented end-user debugging of naive bayes text classification
Todd Kulesza, Simone Stumpf, Weng-Keen Wong, Margaret M Burnett, Stephen Perona, Andrew Ko, and Ian Oberst · 2011
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Visual interpretation of kernel-based prediction models
Katja Hansen, David Baehrens, Timon Schroeter, Matthias Rupp, and Klaus-Robert Müller · 2011
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Making machine learning models interpretable
Alfredo Vellido, José David Martín-Guerrero, and Paulo JG Lisboa · 2012
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A generalized taxonomy of explanations styles for traditional and social recommender systems
Alexis Papadimitriou, Panagiotis Symeonidis, and Yannis Manolopoulos · 2012
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Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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Using sensitivity analysis and visualization techniques to open black box data mining models
Paulo Cortez and Mark J Embrechts · 2012
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What has my classifier learned? visualizing the classification rules of bag-of-feature model by support region detection
Lingqiao Liu and Lei Wang · 2012
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Reverse engineering the neural networks for rule extraction in classification problems
M Gethsiyal Augasta and Thangairulappan Kathirvalavakumar · 2012
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Building interpretable classifiers with rules using bayesian analysis
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, and David Madigan · 2012
Cited alongside, same era.
Research directions in interpretable machine learning models
Vanya Van Belle and Paulo Lisboa · 2013
Cited alongside, same era.
Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size
Andreas Backhaus and Udo Seiffert · 2013
Cited alongside, same era.
Interpretability in machine learning–principles and practice
Paulo JG Lisboa · 2013
Cited alongside, same era.
Safe and interpretable machine learning: a methodological review
Clemens Otte · 2013
Cited alongside, same era.
Too much, too little, or just right? ways explanations impact end users’ mental models
Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng-Keen Wong · 2013
Deep saliency: What is learnt by a deep network about saliency?
Sen He and Nicolas Pugeault · 2017
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Towards visual explanations for convolutional neural networks via input resampling
Benjamin J Lengerich, Sandeep Konam, Eric P Xing, Stephanie Rosenthal, and Manuela Veloso · 2017
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Evolutionary visual analysis of deep neural networks
Wen Zhong, Cong Xie, Yuan Zhong, Yang Wang, Wei Xu, Shenghui Cheng, and Klaus Mueller · 2017
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Classification tree extraction from trained artificial neural networks
Andrey Bondarenko, Ludmila Aleksejeva, Vilen Jumutc, and Arkady Borisov · 2017
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Rule extraction from training data using neural network
Saroj Kumar Biswas, Manomita Chakraborty, Biswajit Purkayastha, Pinki Roy, and Dalton Meitei Thounaojam · 2017
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Distilling a neural network into a soft decision tree
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Cited alongside, same era.
Being transparent about transparency
Joseph B Lyons · 2013
Cited alongside, same era.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Cited alongside, same era.
Accurate intelligible models with pairwise interactions
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker · 2013
Cited alongside, same era.
Explanation and reliability of individual predictions
Igor Kononenko, Erik Štrumbelj, Zoran Bosnić, Darko Pevec, Matjaž Kukar, and Marko Robnik-Šikonja · 2013
Cited alongside, same era.
An interpretable stroke prediction model using rules and bayesian analysis
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, and David Madigan · 2013
Cited alongside, same era.
An interpretable classification rule mining algorithm
Alberto Cano, Amelia Zafra, and SebastiáN Ventura · 2013
Cited alongside, same era.
Nicholas Frosst and Geoffrey Hinton · 2017
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Automatic rule extraction from long short term memory networks
W James Murdoch and Arthur Szlam · 2017
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Unsupervised neural-symbolic integration
Son N Tran · 2017
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Interpnet: Neural introspection for interpretable deep learning
Shane Barratt · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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Towards explainable text classification by jointly learning lexicon and modifier terms
Jérémie Clos, Nirmalie Wiratunga, and Stewart Massie · 2017
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A bayesian framework for learning rule sets for interpretable classification
Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, and Perry MacNeille · 2017
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Learning interpretable classification rules with boolean compressed sensing
Dmitry M Malioutov, Kush R Varshney, Amin Emad, and Sanjeeb Dash · 2017
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Interpretable predictions of tree-based ensembles via actionable feature tweaking
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas · 2017
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An interpretable classification framework for information extraction from online healthcare forums
Jun Gao, Ninghao Liu, Mark Lawley, and Xia Hu · 2017
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Towards compact interpretable models: Shrinking of learned probabilistic sentential decision diagrams
Yitao Liang and Guy Van den Broeck · 2017
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Unsupervised does not mean uninterpretable: The case for word sense induction and disambiguation
Alexander Panchenko, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, and Chris Biemann · 2017
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2017
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Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Interpretable human action recognition in compressed domain
Vignesh Srinivasan, Sebastian Lapuschkin, Cornelius Hellge, Klaus-Robert Müller, and Wojciech Samek · 2017
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Visualizing and understanding neural machine translation
Yanzhuo Ding, Yang Liu, Huanbo Luan, and Maosong Sun · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
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Asking “why” in ai: Explainability of intelligent systems–perspectives and challenges
Alun Preece · 2018
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What should be in an xai explanation? what ift reveals
Jonathan Dodge, Sean Penney, Andrew Anderson, and Margaret M Burnett · 2018
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Explainable artificial intelligence: A survey
Filip Karlo Došilović, Mario Brčić, and Nikica Hlupić · 2018
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Visual analytics for explainable deep learning
Jaegul Choo and Shixia Liu · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Explanation in artificial intelligence: insights from the social sciences
Tim Miller · 2018
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Explainable software analytics
Hoa Khanh Dam, Truyen Tran, and Aditya Ghose · 2018
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Explaining explanation for “explainable ai”
Robert R Hoffman, Gary Klein, and Shane T Mueller · 2018
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Visual interpretability for deep learning: a survey
Quan-shi Zhang and Song-Chun Zhu · 2018
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Paving the way to explainable artificial intelligence with fuzzy modeling
Corrado Mencar and José M Alonso · 2018
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The mythos of model interpretability
Zachary C Lipton · 2018
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Designing explainability of an artificial intelligence system
Taehyun Ha, Sangwon Lee, and Sangyeon Kim · 2018
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Explainable ai: the new 42?
Randy Goebel, Ajay Chander, Katharina Holzinger, Freddy Lecue, Zeynep Akata, Simone Stumpf, Peter Kieseberg, and Andreas Holzinger · 2018
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Evaluating explanations by cognitive value
Ajay Chander and Ramya Srinivasan · 2018
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Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda
Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y Lim, and Mohan Kankanhalli · 2018
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Measures of model interpretability for model selection
André Carrington, Paul Fieguth, and Helen Chen · 2018
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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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A comparison study on rule extraction from neural network ensembles, boosted shallow trees, and svms
Guido Bologna and Yoichi Hayashi · 2018
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Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Distill-and-compare: Auditing black-box models using transparent model distillation
Sarah Tan, Rich Caruana, Giles Hooker, and Yin Lou · 2018
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Explanation of prediction models with explain prediction
Marko Robnik-Šikonja · 2018
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Auditing black-box models for indirect influence
Philip Adler, Casey Falk, Sorelle A Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian · 2018
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Towards dependable and explainable machine learning using automated reasoning
Hadrien Bride, Jie Dong, Jin Song Dong, and Zhé Hóu · 2018
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Visualizing the feature importance for black box models
Giuseppe Casalicchio, Christoph Molnar, and Bernd Bischl · 2018
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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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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
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Towards explanation of dnn-based prediction with guided feature inversion
Mengnan Du, Ninghao Liu, Qingquan Song, and Xia Hu · 2018
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Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
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Learning how to explain neural networks: Patternnet and patternattribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne · 2018
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Exact and consistent interpretation for piecewise linear neural networks: A closed form solution
Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, and Jian Pei · 2018
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Interpretable deep convolutional neural networks via meta-learning
Xuan Liu, Xiaoguang Wang, and Stan Matwin · 2018
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Symbolic graph reasoning meets convolutions
Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, and Eric P Xing · 2018
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The building blocks of interpretability
Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev · 2018
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A cti v is: Visual exploration of industry-scale deep neural network models
Minsuk Kahng, Pierre Y Andrews, Aditya Kalro, and Duen Horng Polo Chau · 2018
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Seq2seq-vis: A visual debugging tool for sequence-to-sequence models
Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, and Alexander M Rush · 2018
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A rule extraction study based on a convolutional neural network
Guido Bologna · 2018
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Beyond sparsity: Tree regularization of deep models for interpretability
Mike Wu, Michael C Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez · 2018
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Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
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Regular inference on artificial neural networks
Franz Mayr and Sergio Yovine · 2018
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Building more explainable artificial intelligence with argumentation
Zhiwei Zeng, Chunyan Miao, Cyril Leung, and Jing Jih Chin · 2018
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Learning fuzzy relations and properties for explainable artificial intelligence
Régis Pierrard, Jean-Philippe Poli, and Céline Hudelot · 2018
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Making tree ensembles interpretable: A bayesian model selection approach
Satoshi Hara and Kohei Hayashi · 2018
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Interpreting tree ensembles with intrees
Houtao Deng · 2018
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Developing transparent credit risk scorecards more effectively: An explainable artificial intelligence approach
Gerald Fahner · 2018
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Explainable ai: The promise of genetic programming multi-run subtree encapsulation
Daniel Howard and Mark A Edwards · 2018
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Sanity checks for saliency maps
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Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Explainable artificial intelligence for neuroscience: Behavioral neurostimulation
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Evaluating recurrent neural network explanations
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Explainable artificial intelligence applications in nlp, biomedical, and malware classification: A literature review
Sherin Mary Mathews · 2019
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Counterfactuals in explainable artificial intelligence (xai): evidence from human reasoning
R Byrne · 2019
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Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?
Alberto Fernandez, Francisco Herrera, Oscar Cordon, Maria Jose del Jesus, and Francesco Marcelloni · 2019
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Explainable ai in industry
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Explainable ai: a brief survey on history, research areas, approaches and challenges
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An integrative 3c evaluation framework for explainable artificial intelligence
Xiaocong Cui, Jung Min Lee, and J Hsieh · 2019
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Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo Müller · 2019
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Dark patterns of explainability, transparency, and user control for intelligent systems
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Why these explanations? selecting intelligibility types for explanation goals
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A grounded interaction protocol for explainable artificial intelligence
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Interestingness elements for explainable reinforcement learning through introspection
Pedro Sequeira, Eric Yeh, and Melinda T Gervasio · 2019
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Explainable argumentation for wellness consultation
Isabel Sassoon, Nadin Kökciyan, Elizabeth Sklar, and Simon Parsons · 2019
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Exploring principled visualizations for deep network attributions
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The pragmatic turn in explainable artificial intelligence (xai)
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Can we do better explanations? a proposal of user-centered explainable ai
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explainer: A visual analytics framework for interactive and explainable machine learning
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Interpreting black box predictions using fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Sanmi Koyejo · 2019
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Visual explanation by interpretation: Improving visual feedback capabilities of deep neural networks
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Twin-systems to explain artificial neural networks using case-based reasoning: comparative tests of feature-weighting methods in ann-cbr twins for xai
Eoin M Kenny and Mark T Keane · 2019
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Gan dissection: Visualizing and understanding generative adversarial networks
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Interactive naming for explaining deep neural networks: A formative study
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Human-centric ai for trustworthy iot systems with explainable multilayer perceptrons
Iván García-Magariño, Rajarajan Muttukrishnan, and Jaime Lloret · 2019
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Transforming convolutional neural network to an interpretable classifier
Martin Tamajka, Wanda Benesova, and Matej Kompanek · 2019
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Evolving rule-based explainable artificial intelligence for unmanned aerial vehicles
Blen M Keneni, Devinder Kaur, Ali Al Bataineh, Vijaya K Devabhaktuni, Ahmad Y Javaid, Jack D Zaientz, and Robert P Marinier · 2019
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Explainable artificial intelligence for kids
Jose M Alonso · 2019
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The dangers of post-hoc interpretability: Unjustified counterfactual explanations
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2019
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Exploring the need for explainable artificial intelligence (xai) in intelligent tutoring systems (its)
Vanessa Putnam and Cristina Conati · 2019
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Explainable deep neural networks for multivariate time series predictions
Roy Assaf and Anika Schumann · 2019
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Artificial intelligence, autonomy, and human-machine teams: Interdependence, context, and explainable ai
William F Lawless, Ranjeev Mittu, Donald Sofge, and Laura Hiatt · 2019
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Explainable artificial intelligence for safe intraoperative decision support
Lauren Gordon, Teodor Grantcharov, and Frank Rudzicz · 2019
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The importance of ensuring artificial intelligence and machine learning can be understood at the human level: Sudha ram
S Ram · 2019
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