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The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems.
Causal interpretability for machine learning - problems, methods and evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, and Huan Liu · 1931
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Extracting refined rules from knowledge-based neural networks
Geoffrey G Towell and Jude W Shavlik · 1993
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A translation approach to portable ontology specifications
Thomas R. Gruber · 1993
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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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Extraction of rules from discrete-time recurrent neural networks
Christian W. Omlin and C.Lee Giles · 1996
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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A bayesian approach to unsupervised one-shot learning of object categories
Li Fe-Fei, Fergus, and Perona · 2003
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Guidelines for performing systematic literature reviews in software engineering
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Adversarial machine learning
Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I.P. Rubinstein, and J. D. Tygar · 2011
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Not exactly: In praise of vagueness
Kees Van Deemter · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Broad agency announcement explainable artificial intelligence (xai)
David Gunning · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Federated learning: Strategies for improving communication efficiency
J. Konečný, H.B. McMahan, F.X. Yu, P. Richtárik, A.T. Suresh, and D. Bacon · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deepred – rule extraction from deep neural networks
Jan Ruben Zilke, Eneldo Loza Mencía, and Frederik Janssen · 2016
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Towards bayesian deep learning: A framework and some existing methods
Hao Wang and Dit-Yan Yeung · 2016
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Doctor ai: Predicting clinical events via recurrent neural networks
Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun · 2016
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Learning from explanations using sentiment and advice in rl
Samantha Krening, Brent Harrison, Karen M. Feigh, Charles Lee Isbell, Mark Riedl, and Andrea Thomaz · 2016
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Explanation and justification in machine learning: A survey
Or Biran and Courtenay Cotton · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
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Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
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" what is relevant in a text document?": An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2017
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Interpretability of deep learning models: A survey of results
Supriyo Chakraborty, Richard Tomsett, Ramya Raghavendra, Daniel Harborne, Moustafa Alzantot, Federico Cerutti, Mani Srivastava, Alun Preece, Simon Julier, Raghuveer M. Rao, Troy D. Kelley, Dave Braines, Murat Sensoy, Christopher J. Willis, and Prudhvi Gurram · 2017
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Maria Fox, Derek Long, and Daniele Magazzeni · 2017
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A systematic review and taxonomy of explanations in decision support and recommender systems
Ingrid Nunes and Dietmar Jannach · 2017
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Thinking, fast and slow, 2017
Kahneman Daniel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Accountability of ai under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O’Brien, Kate Scott, Stuart Schieber, James Waldo, David Weinberger, et al · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Deepeyes: Progressive visual analytics for designing deep neural networks
Nicola Pezzotti, Thomas Höllt, Jan Van Gemert, Boudewijn P.F. Lelieveldt, Elmar Eisemann, and Anna Vilanova · 2017
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Analyzing the training processes of deep generative models
Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, and Shixia Liu · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Humans forget, machines remember: Artificial intelligence and the right to be forgotten
Eduard Fosch Villaronga, Peter Kieseberg, and Tiffany Li · 2017
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Explainable agency for intelligent autonomous systems
Pat Langley, Ben Meadows, Mohan Sridharan, and Dongkyu Choi · 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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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
Cited alongside, same era.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C. Lipton · 2018
Cited alongside, same era.
Interpretable machine learning in healthcare
M. Ahmad, A. Teredesai, and C. Eckert · 2018
Cited alongside, same era.
Asking ‘why’ in ai: Explainability of intelligent systems – perspectives and challenges
Alun Preece · 2018
Cited alongside, same era.
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges , pages 19–36
Gabriëlle Ras, Marcel van Gerven, and Pim Haselager · 2018
Cited alongside, same era.
Explainable artificial intelligence: A survey
Interpretable machine learning – a brief history, state-of-the-art and challenges
Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl · 2020
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On the interpretability of artificial intelligence in radiology: Challenges and opportunities
Mauricio Reyes, Raphael Meier, Sérgio Pereira, Carlos A. Silva, Fried-Michael Dahlweid, Hendrik von Tengg-Kobligk, Ronald M. Summers, and Roland Wiest · 2020
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Explainable artificial intelligence: Concepts, applications, research challenges and visions
Luca Longo, Randy Goebel, Freddy Lecue, Peter Kieseberg, and Andreas Holzinger · 2020
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Survey of XAI in Digital Pathology , pages 56–88
Milda Pocevičiūtė, Gabriel Eilertsen, and Claes Lundström · 2020
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Review study of interpretation methods for future interpretable machine learning
Jian-Xun Mi, An-Di Li, and Li-Fang Zhou · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Filip Karlo Došilović, Mario Brčić, and Nikica Hlupić · 2018
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2018
Cited alongside, same era.
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda , page 1–18
Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli · 2018
Cited alongside, same era.
Current advances, trends and challenges of machine learning and knowledge extraction: From machine learning to explainable ai
Andreas Holzinger, Peter Kieseberg, Edgar Weippl, and A. Min Tjoa · 2018
Cited alongside, same era.
Visual analytics for explainable deep learning
J. Choo and S. Liu · 2018
Cited alongside, same era.
Visual interpretability for deep learning: a survey
Quanshi Zhang and Song-Chun Zhu · 2018
Cited alongside, same era.
Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
Cited alongside, same era.
A survey of data-driven and knowledge-aware explainable ai
Xiao-Hui Li, Caleb Chen Cao, Yuhan Shi, Wei Bai, Han Gao, Luyu Qiu, Cong Wang, Yuanyuan Gao, Shenjia Zhang, Xun Xue, and Lei Chen · 2020
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Explainable deep learning: A field guide for the uninitiated
Ning Xie, Gabrielle Ras, Marcel van Gerven, and Derek Doran · 2020
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Achievements and challenges in explaining deep learning based computer-aided diagnosis systems
Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, and Sheraz Ahmed · 2020
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A survey on deep learning and explainability for automatic image-based medical report generation
Pablo Messina, Pablo Pino, Denis Parra, Alvaro Soto, Cecilia Besa, Sergio Uribe, Cristian Tejos, Claudia Prieto, Daniel Capurro, et al · 2020
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Explainable artificial intelligence: a systematic review
Giulia Vilone and Luca Longo · 2020
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A survey of the state of explainable ai for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen · 2020
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2020
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Demystifying deep neural networks through interpretation: A survey
Giang Dao and Minwoo Lee · 2020
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Explaining the black-box model: A survey of local interpretation methods for deep neural networks
Yu Liang, Siguang Li, Chungang Yan, Maozhen Li, and Changjun Jiang · 2020
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Extract interpretability-accuracy balanced rules from artificial neural networks: A review
Congjie He, Meng Ma, and Ping Wang · 2020
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A survey on explainable artificial intelligence (xai): Toward medical xai
Erico Tjoa and Cuntai Guan · 2020
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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi · 2020
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Explainable recommendation: A survey and new perspectives
Yongfeng Zhang and Xu Chen · 2020
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
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Flexible and context-specific ai explainability: a multidisciplinary approach
Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d’Alché Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, and Jayneel Parekh · 2020
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Dimensions of Data Quality (DDQ), 2020
Andrew Black and Peter Nederpelt · 2020
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Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
Manas Gaur, Keyur Faldu, and Amit Sheth · 2020
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Doctor xai: An ontology-based approach to black-box sequential data classification explanations
Cecilia Panigutti, Alan Perotti, and Dino Pedreschi · 2020
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Ontology engineering: Current state, challenges, and future directions
Tania Tudorache · 2020
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Show us the data: Privacy, explainability, and why the law can’t have both
Thomas D Grant and Damon J Wischik · 2020
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Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, et al · 2020
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H2O AutoML: Scalable automatic machine learning
Erin LeDell and Sebastien Poirier · 2020
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A survey on the explainability of supervised machine learning
Nadia Burkart and Marco F Huber · 2021
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Explainable artificial intelligence approaches: A survey
Sheikh Rabiul Islam, William Eberle, Sheikh Khaled Ghafoor, and Mohiuddin Ahmed · 2021
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Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations , pages 217–267
Boris Kovalerchuk, Muhammad Aurangzeb Ahmad, and Ankur Teredesai · 2021
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On interpretability of artificial neural networks: A survey
Feng-Lei Fan, Jinjun Xiong, Mengzhou Li, and Ge Wang · 2021
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Principles and practice of explainable machine learning
Vaishak Belle and Ioannis Papantonis · 2021
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A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
Ilia Stepin, Jose M. Alonso, Alejandro Catala, and Martín Pereira-Fariña · 2021
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Explainable ai and reinforcement learning—a systematic review of current approaches and trends
Lindsay Wells and Tomasz Bednarz · 2021
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Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb · 2021
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A survey on incorporating domain knowledge into deep learning for medical image analysis
Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, and Shui Yu · 2021
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Using ontologies to enhance human understandability of global post-hoc explanations of black-box models
Roberto Confalonieri, Tillman Weyde, Tarek R. Besold, and Fermín Moscoso del Prado Martín · 2021
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Mljar: State-of-the-art automated machine learning framework for tabular data. version 0.10.3, 2021
Aleksandra Płońska and Piotr Płoński · 2021
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Machine learning interpretability: A survey on methods and metrics
Diogo V. Carvalho, Eduardo M. Pereira, and Jaime S. Cardoso · 2079
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