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Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature.
Interpretable machine learning: definitions, methods, and applications
Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, R.; and Yu, B. 2019 · 1901
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Personalized explanation in machine learning: A conceptualization
Schneider, J.; and Handali, J. 2019 · 1901
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Ask not what AI can do, but what AI should do: Towards a framework of task delegability
Lubars, B.; and Tan, C. 2019 · 1902
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Mueller, S. T.; Hoffman, R. R.; Clancey, W.; Emrey, A.; and Klein, G. 2019 · 1902
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One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
Arya, V.; Bellamy, R. K.; Chen, P.-Y.; Dhurandhar, A.; Hind, M.; Hoffman, S. C.; Houde, S.; Liao, Q. V.; Luss, R.; Mojsilović, A.; et al. 2019 · 1909
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Implications of using Likert data in multiple regression analysis
Owuor, C. O. 2001 · 2001
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Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
Hase, P.; and Bansal, M. 2020 · 2005
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Evaluations and methods for explanation through robustness analysis
Hsieh, C.-Y.; Yeh, C.-K.; Liu, X.; Ravikumar, P.; Kim, S.; Kumar, S.; and Hsieh, C.-J. 2020 · 2006
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Explanation and understanding
Keil, F. C. 2006 · 2006
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The structure and function of explanations
Lombrozo, T. 2006 · 2006
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Explanation and abductive inference
Lombrozo, T. 2012 · 2012
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Too much, too little, or just right? Ways explanations impact end users’ mental models
Kulesza, T.; Stumpf, S.; Burnett, M.; Yang, S.; Kwan, I.; and Wong, W.-K. 2013 · 2013
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Principles of explanatory debugging to personalize interactive machine learning
Kulesza, T.; Burnett, M.; Wong, W.-K.; and Stumpf, S. 2015 · 2015
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Goals Affect the Perceived Quality of Explanations
Vasilyeva, N.; Wilkenfeld, D. A.; and Lombrozo, T. 2015 · 2015
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F.; and Kim, B. 2017 · 2017
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Miller, T.; Howe, P.; and Sonenberg, L. 2017 · 2017
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Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)
Adadi, A.; and Berrada, M. 2018 · 2018
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Towards robust interpretability with self-explaining neural networks
Alvarez-Melis, D.; and Jaakkola, T. S. 2018 · 2018
Earlier work this paper cites.
Explaining explanations: An overview of interpretability of machine learning
Gilpin, L. H.; Bau, D.; Yuan, B. Z.; Bajwa, A.; Specter, M.; and Kagal, L. 2018 · 2018
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A survey of methods for explaining black box models
Guidotti, R.; Monreale, A.; Ruggieri, S.; Turini, F.; Giannotti, F.; and Pedreschi, D. 2018 · 2018
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Metrics for explainable AI: Challenges and prospects
Hoffman, R. R.; Mueller, S. T.; Klein, G.; and Litman, J. 2018 · 2018
Cited alongside, same era.
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery
Lipton, Z. C. 2018 · 2018
Cited alongside, same era.
A multidisciplinary survey and framework for design and evaluation of explainable AI systems
Mohseni, S.; Zarei, N.; and Ragan, E. D. 2018 · 2018
Cited alongside, same era.
Stakeholders in explainable AI
Preece, A.; Harborne, D.; Braines, D.; Tomsett, R.; and Chakraborty, S. 2018 · 2018
Cited alongside, same era.
Explanation methods in deep learning: Users, values, concerns and challenges
Ras, G.; van Gerven, M.; and Haselager, P. 2018 · 2018
Human factors in model interpretability: Industry practices, challenges, and needs
Hong, S. R.; Hullman, J.; and Bertini, E. 2020 · 2020
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How can i explain this to you? an empirical study of deep neural network explanation methods
Jeyakumar, J. V.; Noor, J.; Cheng, Y.-H.; Garcia, L.; and Srivastava, M. 2020 · 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Kaur, H.; Nori, H.; Jenkins, S.; Caruana, R.; Wallach, H.; and Wortman Vaughan, J. 2020 · 2020
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” How do I fool you?” Manipulating User Trust via Misleading Black Box Explanations
Lakkaraju, H.; and Bastani, O. 2020 · 2020
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Questioning the AI: informing design practices for explainable AI user experiences
Liao, Q. V.; Gruen, D.; and Miller, S. 2020 · 2020
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Cited alongside, same era.
The effects of example-based explanations in a machine learning interface
Cai, C. J.; Jongejan, J.; and Holbrook, J. 2019 · 2019
Cited alongside, same era.
Machine learning interpretability: A survey on methods and metrics
Carvalho, D. V.; Pereira, E. M.; and Cardoso, J. S. 2019 · 2019
Cited alongside, same era.
Explaining models: an empirical study of how explanations impact fairness judgment
Dodge, J.; Liao, Q. V.; Zhang, Y.; Bellamy, R. K.; and Dugan, C. 2019 · 2019
Cited alongside, same era.
XAI—Explainable artificial intelligence
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; and Yang, G.-Z. 2019 · 2019
Cited alongside, same era.
Explaining explainable AI
Hind, M. 2019 · 2019
Cited alongside, same era.
Gamut: A design probe to understand how data scientists understand machine learning models
Hohman, F.; Head, A.; Caruana, R.; DeLine, R.; and Drucker, S. M. 2019 · 2019
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. 2019 · 2019
Cited alongside, same era.
Why does my model fail? contrastive local explanations for retail forecasting
Lucic, A.; Haned, H.; and de Rijke, M. 2020 · 2020
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Interpretable machine learning
Molnar, C. 2020 · 2020
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Explainability fact sheets: a framework for systematic assessment of explainable approaches
Sokol, K.; and Flach, P. 2020 · 2020
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CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis
Xie, Y.; Chen, M.; Kao, D.; Gao, G.; and Chen, X. 2020 · 2020
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Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
Zhang, Y.; Liao, Q. V.; and Bellamy, R. K. 2020 · 2020
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Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Bansal, G.; Wu, T.; Zhou, J.; Fok, R.; Nushi, B.; Kamar, E.; Ribeiro, M. T.; and Weld, D. 2021 · 2021
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Bhatt, U.; Antorán, J.; Zhang, Y.; Liao, Q. V.; Sattigeri, P.; Fogliato, R.; Melançon, G.; Krishnan, R.; Stanley, J.; Tickoo, O.; et al. 2021 · 2021
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Towards Connecting Use Cases and Methods in Interpretable Machine Learning
Chen, V.; Li, J.; Kim, J. S.; Plumb, G.; and Talwalkar, A. 2021 · 2021
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Explainability Pitfalls: Beyond Dark Patterns in Explainable AI
Ehsan, U.; and Riedl, M. O. 2021 · 2021
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Ghosh, S.; Liao, Q. V.; Ramamurthy, K. N.; Navratil, J.; Sattigeri, P.; Varshney, K. R.; and Zhang, Y. 2021 · 2021
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How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
Jesus, S.; Belém, C.; Balayan, V.; Bento, J.; Saleiro, P.; Bizarro, P.; and Gama, J. 2021 · 2021
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Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
Liao, Q. V.; and Varshney, K. R. 2021 · 2021
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Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Narkar, S.; Zhang, Y.; Liao, Q. V.; Wang, D.; and Weisz, J. D. 2021 · 2021
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Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
Suresh, H.; Gomez, S. R.; Nam, K. K.; and Satyanarayan, A. 2021 · 2021
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Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
Wang, X.; and Yin, M. 2021 · 2021
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