Most cited papers in Explainable Artificial Intelligence (XAI)
What are the most cited papers in Explainable Artificial Intelligence (XAI)?
The most cited paper in Explainable Artificial Intelligence (XAI) is "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization" (Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Kumar Das et al., 2017), with 23,064 citations. Next come ""Why Should I Trust You?"" (16,496) and "Visualizing and Understanding Convolutional Networks" (15,691).
Showing 50 of 99 papers. Every paper with more than 968 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Kumar Das et al. | Oct 2017 | 23,064 |
| 2 | "Why Should I Trust You?" Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin | 8 Aug 2016 | 16,496 |
| 3 | Visualizing and Understanding Convolutional Networks Matthew D. Zeiler, Rob Fergus · Lecture notes in computer science | 2014 | 15,691 |
| 4 | Generative adversarial networks Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza et al. · Communications of the ACM | 22 Oct 2020 | 14,417 |
| 5 | On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper) Gauriat, Charles-Maxime, Pencolé, Yannick, Ribot, Pauline et al. · OASIcs : OpenAccess Series in Informatics | 2024 | 13,571 |
| 6 | From local explanations to global understanding with explainable AI for trees Scott Lundberg, Gabriel Erion, Hugh Chen et al. · Nature Machine Intelligence | 17 Jan 2020 | 10,292 |
| 7 | Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Cynthia D Rudin · Nature Machine Intelligence | 13 May 2019 | 10,230 |
| 8 | Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser et al. · Information Fusion | 26 Dec 2019 | 10,008 |
| 9 | Explaining and Harnessing Adversarial Examples Ian Goodfellow, Jonathon Shlens, Christian Szegedy · arXiv (Cornell University) | 20 Dec 2014 | 8,140 |
| 10 | A Unified Approach to Interpreting Model Predictions Scott Lundberg, Su‐In Lee · arXiv (Cornell University) | 22 May 2017 | 7,625 |
| 11 | Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) Amina Adadi, Mohammed Berrada · IEEE Access | 2018 | 6,253 |
| 12 | “Why Should I Trust You?”: Explaining the Predictions of Any Classifier Marco Ribeiro, Sameer Kumar Singh, Carlos Guestrin | 2016 | 5,530 |
| 13 | DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard | Jun 2016 | 5,406 |
| 14 | A survey of methods for explaining black box models Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri et al. · ISTI Open Portal | 2019 | 5,066 |
| 15 | On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation Sebastian Bach, Alexander Binder, Grégoire Montavon et al. · PLoS ONE | 10 Jul 2015 | 4,700 |
| 16 | The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot, Patrick Drew McDaniel, Somesh Jha et al. | Mar 2016 | 3,986 |
| 17 | Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader et al. | Mar 2018 | 3,262 |
| 18 | Key challenges for delivering clinical impact with artificial intelligence Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman et al. · BMC Medicine | 29 Oct 2019 | 3,030 |
| 19 | Explainable AI: A Review of Machine Learning Interpretability Methods Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris B. Kotsiantis · Entropy | 25 Dec 2020 | 2,932 |
| 20 | Permutation importance: a corrected feature importance measure André Altmann, Laura Toloşi, Oliver Sander et al. · Bioinformatics | 12 Apr 2010 | 2,910 |
| 21 | What is AI Literacy? Competencies and Design Considerations Duri Long, Brian Magerko | 21 Apr 2020 | 2,879 |
| 22 | Methods for interpreting and understanding deep neural networks Grégoire Montavon, Wojciech Samek, Klaus‐Robert Müller · Digital Signal Processing | 24 Oct 2017 | 2,780 |
| 23 | Axiomatic Attribution for Deep Networks Mukund Sundararajan, Ankur Taly, Qiqi Yan · arXiv (Cornell University) | 4 Mar 2017 | 2,648 |
| 24 | A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI Erico Tjoa, Cuntai Guan · IEEE Transactions on Neural Networks and Learning Systems | 21 Oct 2020 | 2,419 |
| 25 | Calibration: the Achilles heel of predictive analytics On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of , Ben Van Calster, David J. McLernon et al. · BMC Medicine | Dec 2019 | 2,384 |
| 26 | Learning Important Features Through Propagating Activation Differences Avanti Shrikumar, Peyton Greenside, Anshul Kundaje · arXiv (Cornell University) | 10 Apr 2017 | 2,377 |
| 27 | Big Data and Machine Learning in Health Care Andrew L. Beam, Isaac S. Kohane · JAMA | 12 Mar 2018 | 2,347 |
| 28 | Explaining prediction models and individual predictions with feature contributions Erik Štrumbelj, Igor Kononenko · Knowledge and Information Systems | 29 Aug 2013 | 2,336 |
| 29 | Definitions, methods, and applications in interpretable machine learning William J. Murdoch, Chandan Singh, Karl Kumbier et al. · Proceedings of the National Academy of Sciences | 16 Oct 2019 | 2,179 |
| 30 | Explainability for artificial intelligence in healthcare: a multidisciplinary perspective Julia Amann, Alessandro Blasimme, Effy Vayena et al. · BMC Medical Informatics and Decision Making | 30 Nov 2020 | 2,163 |
| 31 | Anchors: High-Precision Model-Agnostic Explanations Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin · Proceedings of the AAAI Conference on Artificial Intelligence | 25 Apr 2018 | 2,143 |
| 32 | A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models Evangelia F. CHRISTODOULOU, Jie Ma, Professor Gary S. Collins et al. · Journal of Clinical Epidemiology | 11 Feb 2019 | 2,132 |
| 33 | Confirmation, disconfirmation, and information in hypothesis testing. Joshua Klayman, Young-Won Ha · Psychological Review | Apr 1987 | 2,073 |
| 34 | Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence Vikas Hassija, Vinay Chamola, Atmesh Mahapatra et al. · Cognitive Computation | 24 Aug 2023 | 2,031 |
| 35 | XAI—Explainable artificial intelligence David Gunning, Mark Stefik, Jaesik Choi et al. · Science Robotics | 18 Dec 2019 | 2,024 |
| 36 | The preregistration revolution Brian A. Nosek, Charles R. Ebersole, Alexander Carl DeHaven et al. · Proceedings of the National Academy of Sciences | 12 Mar 2018 | 2,019 |
| 37 | Improved Boosting Algorithms Using Confidence-rated Predictions Robert E. Schapire, Yoram Singer · Machine Learning | Dec 1999 | 1,971 |
| 38 | Algorithm appreciation: People prefer algorithmic to human judgment Jennifer Marie Logg, Julia Alexandra Minson, Don A. Moore · Organizational Behavior and Human Decision Processes | 5 Feb 2019 | 1,941 |
| 39 | Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, Nathan Srebro · arXiv (Cornell University) | 7 Oct 2016 | 1,916 |
| 40 | ChatGPT: five priorities for research Eva A.M. van Dis, Johan Bollen, Willem H. Zuidema et al. · Nature | 3 Feb 2023 | 1,886 |
| 41 | Causability and explainability of artificial intelligence in medicine Andreas Holzinger, Georg Langs, Helmut Denk et al. · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery | 2 Apr 2019 | 1,872 |
| 42 | Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation Marina V. Sokolova, Nathalie Japkowicz, Stan Śzpakowicz · Lecture notes in computer science | 2006 | 1,861 |
| 43 | Machine Learning Interpretability: A Survey on Methods and Metrics Diogo V. Carvalho, Eduardo M. Pereira, Jaime S. Cardoso · Electronics | 26 Jul 2019 | 1,837 |
| 44 | The Ethics of AI Ethics: An Evaluation of Guidelines Thilo Hagendorff · Minds and Machines | Feb 2020 | 1,783 |
| 45 | TabNet: Attentive Interpretable Tabular Learning Sercan Ö. Arık, Tomas Pfister · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 18 May 2021 | 1,746 |
| 46 | Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence Sajid Ali, Tamer Abuhmed, Shaker El–Sappagh et al. · Information Fusion | 18 Apr 2023 | 1,716 |
| 47 | Intelligible Models for HealthCare Rich Caruana, Yin Lou, Johannes E. Gehrke et al. | 7 Aug 2015 | 1,700 |
| 48 | Artificial intelligence: A powerful paradigm for scientific research Yongjun Xu et al. · The Innovation | 28 Oct 2021 | 1,696 |
| 49 | The false hope of current approaches to explainable artificial intelligence in health care Marzyeh Ghassemi, Luke Oakden‐Rayner, Andrew L. Beam · The Lancet Digital Health | 25 Oct 2021 | 1,643 |
| 50 | Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods Eyke Hüllermeier, Willem Waegeman · Machine Learning | Mar 2021 | 1,637 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.