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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:

Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies
Eoin M. Kenny, Courtney Ford, Molly S. Quinn, et al.
Artificial Intelligence (2021) Vol. 294, pp. 103459-103459
Open Access | Times Cited: 162

Showing 1-25 of 162 citing articles:

Explainable artificial intelligence: a comprehensive review
Dang Lien Minh, Hanxiang Wang, Yanfen Li, et al.
Artificial Intelligence Review (2021) Vol. 55, Iss. 5, pp. 3503-3568
Closed Access | Times Cited: 427
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review
Anna Markella Antoniadi, Yuhan Du, Yasmine Guendouz, et al.
Applied Sciences (2021) Vol. 11, Iss. 11, pp. 5088-5088
Open Access | Times Cited: 378
Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations
Anastasiya Kiseleva, Dimitris Kotzinos, Paul De Hert
Frontiers in Artificial Intelligence (2022) Vol. 5
Open Access | Times Cited: 108
Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
Weiping Ding, Mohamed Abdel‐Basset, Hossam Hawash, et al.
Information Sciences (2022) Vol. 615, pp. 238-292
Closed Access | Times Cited: 107
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability
Lukas-Valentin Herm, Kai Heinrich, Jonas Wanner, et al.
International Journal of Information Management (2022) Vol. 69, pp. 102538-102538
Open Access | Times Cited: 83
How transparency modulates trust in artificial intelligence
John Zerilli, Umang Bhatt, Adrian Weller
Patterns (2022) Vol. 3, Iss. 4, pp. 100455-100455
Open Access | Times Cited: 82
Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review
Anirban Adak, Biswajeet Pradhan, Nagesh Shukla
Foods (2022) Vol. 11, Iss. 10, pp. 1500-1500
Open Access | Times Cited: 82
A survey on XAI and natural language explanations
Erik Cambria, Lorenzo Malandri, Fabio Mercorio, et al.
Information Processing & Management (2022) Vol. 60, Iss. 1, pp. 103111-103111
Closed Access | Times Cited: 76
Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task
Benedikt Leichtmann, Christina Humer, Andreas Hinterreiter, et al.
Computers in Human Behavior (2022) Vol. 139, pp. 107539-107539
Open Access | Times Cited: 74
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
Eoin M. Kenny, Mark T. Keane
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 13, pp. 11575-11585
Open Access | Times Cited: 71
Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making
Oskar Wysocki, Jessica Katharine Davies, Markel Vigo, et al.
Artificial Intelligence (2022) Vol. 316, pp. 103839-103839
Open Access | Times Cited: 64
The effects of domain knowledge on trust in explainable AI and task performance: A case of peer-to-peer lending
Murat Dikmen, Catherine M. Burns
International Journal of Human-Computer Studies (2022) Vol. 162, pp. 102792-102792
Open Access | Times Cited: 50
Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review
Jinsun Jung, Hyungbok Lee, Hyunggu Jung, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16110-e16110
Open Access | Times Cited: 38
An improved explainable artificial intelligence tool in healthcare for hospital recommendation
Yu-Cheng Wang, Toly Chen, Min-Chi Chiu
Healthcare Analytics (2023) Vol. 3, pp. 100147-100147
Open Access | Times Cited: 34
XAIR: A Framework of Explainable AI in Augmented Reality
Xuhai Xu, Anna Yu, Tanya R. Jonker, et al.
(2023), pp. 1-30
Open Access | Times Cited: 28
Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
Yu An, Haiwen Du, Siteng Ma, et al.
Earth-Science Reviews (2023) Vol. 243, pp. 104509-104509
Open Access | Times Cited: 23
Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review
Harishankar Vasudevanallur Subramanian, Casey Canfield, Daniel B. Shank
Artificial Intelligence in Medicine (2024) Vol. 149, pp. 102780-102780
Open Access | Times Cited: 12
Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems
Marjorie Kinney, Maria Anastasiadou, Mijail Naranjo-Zolotov, et al.
Heliyon (2024) Vol. 10, Iss. 7, pp. e28562-e28562
Open Access | Times Cited: 11
BMB-LIME: LIME with modeling local nonlinearity and uncertainty in explainability
Yu-Hsin Hung, Chia‐Yen Lee
Knowledge-Based Systems (2024) Vol. 294, pp. 111732-111732
Closed Access | Times Cited: 8
Integrated Gradient-Based Continuous Wavelet Transform for Bearing Fault Diagnosis
Junfei Du, Xinyu Li, Yiping Gao, et al.
Sensors (2022) Vol. 22, Iss. 22, pp. 8760-8760
Open Access | Times Cited: 32
Effects of Explanations in AI-Assisted Decision Making: Principles and Comparisons
Xinru Wang, Ming Yin
ACM Transactions on Interactive Intelligent Systems (2022) Vol. 12, Iss. 4, pp. 1-36
Open Access | Times Cited: 31
Numerical Grad-Cam Based Explainable Convolutional Neural Network for Brain Tumor Diagnosis
José Antonio Marmolejo-Saucedo, Utku Köse
Mobile Networks and Applications (2022) Vol. 29, Iss. 1, pp. 109-118
Closed Access | Times Cited: 31
Interpretability of Machine Learning: Recent Advances and Future Prospects
Lei Gao, Ling Guan
IEEE Multimedia (2023) Vol. 30, Iss. 4, pp. 105-118
Open Access | Times Cited: 20
Categorical and Continuous Features in Counterfactual Explanations of AI Systems
Greta Warren, Ruth M. J. Byrne, Mark T. Keane
(2023)
Open Access | Times Cited: 19
How people reason with counterfactual and causal explanations for Artificial Intelligence decisions in familiar and unfamiliar domains
Lenart Celar, Ruth M. J. Byrne
Memory & Cognition (2023) Vol. 51, Iss. 7, pp. 1481-1496
Open Access | Times Cited: 18
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