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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, et al.
(2020), pp. 1-14
Closed Access | Times Cited: 402

Showing 1-25 of 402 citing articles:

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 (2021) Vol. 3, Iss. 11, pp. e745-e750
Open Access | Times Cited: 732
Interpretable machine learning: Fundamental principles and 10 grand challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, et al.
Statistics Surveys (2022) Vol. 16, Iss. none
Open Access | Times Cited: 565
Explainable Deep Learning Models in Medical Image Analysis
Amitojdeep Singh, Sourya Sengupta, Vasudevan Lakshminarayanan
Journal of Imaging (2020) Vol. 6, Iss. 6, pp. 52-52
Open Access | Times Cited: 536
Interpretable Machine Learning
Valerie Chen, Jeffrey Li, Joon Sik Kim, et al.
Queue (2021) Vol. 19, Iss. 6, pp. 28-56
Open Access | Times Cited: 387
Notions of explainability and evaluation approaches for explainable artificial intelligence
Giulia Vilone, Luca Longo
Information Fusion (2021) Vol. 76, pp. 89-106
Open Access | Times Cited: 386
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou, et al.
(2021), pp. 1-16
Open Access | Times Cited: 364
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, et al.
(2021), pp. 1-52
Open Access | Times Cited: 350
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
Alon Jacovi, Yoav Goldberg
(2020)
Open Access | Times Cited: 323
Formalizing Trust in Artificial Intelligence
Alon Jacovi, Ana Marasović, Tim Miller, et al.
(2021), pp. 624-635
Closed Access | Times Cited: 296
Interpretable Machine Learning
Brad Boehmke, Brandon Greenwell
Chapman and Hall/CRC eBooks (2019), pp. 305-342
Open Access | Times Cited: 256
To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis
Sarah Lebovitz, Hila Lifshitz‐Assaf, Natalia Levina
Organization Science (2022) Vol. 33, Iss. 1, pp. 126-148
Open Access | Times Cited: 242
Interpretable machine learning
Valerie Chen, Jeffrey Li, Joon Sik Kim, et al.
Communications of the ACM (2022) Vol. 65, Iss. 8, pp. 43-50
Open Access | Times Cited: 208
Algorithmic fairness in artificial intelligence for medicine and healthcare
Richard J. Chen, Judy J. Wang, Drew F. K. Williamson, et al.
Nature Biomedical Engineering (2023) Vol. 7, Iss. 6, pp. 719-742
Open Access | Times Cited: 198
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna, Tessa Han, Alex Gu, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 108
Explanations Can Reduce Overreliance on AI Systems During Decision-Making
Helena Vasconcelos, Matthew Jörke, Madeleine Grunde-McLaughlin, et al.
Proceedings of the ACM on Human-Computer Interaction (2023) Vol. 7, Iss. CSCW1, pp. 1-38
Open Access | Times Cited: 100
How Cognitive Biases Affect XAI-assisted Decision-making
Astrid Bertrand, Rafik Belloum, James Eagan, et al.
(2022), pp. 78-91
Open Access | Times Cited: 72
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences
Mara Graziani, L. Dutkiewicz, Davide Calvaresi, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 4, pp. 3473-3504
Open Access | Times Cited: 72
Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
Yao Rong, Tobias Leemann, Thai-trang Nguyen, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2023) Vol. 46, Iss. 4, pp. 2104-2122
Open Access | Times Cited: 67
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, et al.
Proceedings of the ACM on Human-Computer Interaction (2023) Vol. 7, Iss. CSCW2, pp. 1-32
Open Access | Times Cited: 61
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
Xiaojuan Ma, Ying Lei, Xinru Wang, et al.
(2023), pp. 1-19
Open Access | Times Cited: 58
The role of explainable AI in the context of the AI Act
Cecilia Panigutti, Ronan Hamon, Isabelle Hupont, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2023), pp. 1139-1150
Open Access | Times Cited: 55
Explaining machine learning models with interactive natural language conversations using TalkToModel
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 8, pp. 873-883
Open Access | Times Cited: 44
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Upol Ehsan, Samir Passi, Q. Vera Liao, et al.
(2024), pp. 1-32
Open Access | Times Cited: 19
A comprehensive review on financial explainable AI
Wei Jie Yeo, Wihan van der Heever, Rui Mao, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 6
Open Access | Times Cited: 2
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI
Richard Tomsett, Alun Preece, Dave Braines, et al.
Patterns (2020) Vol. 1, Iss. 4, pp. 100049-100049
Open Access | Times Cited: 101
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