Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Showing 1-25 of 72 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: 439
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
Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning
Gabriel R. Vásquez-Morales, Sergio Mauricio Martínez Monterrubio, Pablo Moreno‐Ger, et al.
IEEE Access (2019) Vol. 7, pp. 152900-152910
Open Access | Times Cited: 112
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
Mark T. Keane, Barry Smyth
Lecture notes in computer science (2020), pp. 163-178
Closed Access | Times Cited: 109
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
Instance-Based Counterfactual Explanations for Time Series Classification
Eoin Delaney, Derek Greene, Mark T. Keane
Lecture notes in computer science (2021), pp. 32-47
Closed Access | Times Cited: 71
Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time
Shaker El–Sappagh, Hager Saleh, Farman Ali, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 17, pp. 14487-14509
Closed Access | Times Cited: 62
State of the Art of Visual Analytics for eXplainable Deep Learning
Biagio La Rosa, Graziano Blasilli, Romain Bourqui, et al.
Computer Graphics Forum (2023) Vol. 42, Iss. 1, pp. 319-355
Open Access | Times Cited: 37
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
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
Explainable screening of oral cancer via deep learning and case-based reasoning
Mario G. C. A. Cimino, Giuseppina Campisi, Federico A. Galatolo, et al.
Smart Health (2025), pp. 100538-100538
Closed Access | Times Cited: 1
Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI
Eoin M. Kenny, Mark T. Keane
(2019), pp. 2708-2715
Open Access | Times Cited: 63
A case-based reasoning framework for early detection and diagnosis of novel coronavirus
Olaide N. Oyelade, Absalom E. Ezugwu
Informatics in Medicine Unlocked (2020) Vol. 20, pp. 100395-100395
Open Access | Times Cited: 59
Explainable AI and Law: An Evidential Survey
Karen McGregor Richmond, Satya M. Muddamsetty, Thomas Gammeltoft-­Hansen, et al.
Deleted Journal (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 16
Mitigating belief projection in explainable artificial intelligence via Bayesian teaching
Scott Cheng‐Hsin Yang, Wai Keen Vong, Ravi B. Sojitra, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 36
Explainable AI, But Explainable to Whom? An Exploratory Case Study of xAI in Healthcare
Julie Gerlings, Millie Søndergaard Jensen, Arisa Shollo
Intelligent systems reference library (2021), pp. 169-198
Closed Access | Times Cited: 34
Explainable artificial intelligence, lawyer's perspective
Łukasz Górski, Shashishekar Ramakrishna
(2021), pp. 60-68
Closed Access | Times Cited: 27
Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI
Eoin M. Kenny, Mark T. Keane
Knowledge-Based Systems (2021) Vol. 233, pp. 107530-107530
Open Access | Times Cited: 27
Understanding imbalanced data: XAI & interpretable ML framework
Damien Dablain, Colin Bellinger, Bartosz Krawczyk, et al.
Machine Learning (2024) Vol. 113, Iss. 6, pp. 3751-3769
Open Access | Times Cited: 4
Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework
Aorigele Bao, Yi Zeng
Humanities and Social Sciences Communications (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 4
Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis
Helena Montenegro, Wilson Silva, Jaime S. Cardoso
IEEE Access (2021) Vol. 9, pp. 148037-148047
Open Access | Times Cited: 25
Examining explainable clinical decision support systems with think aloud protocols
Sabrina Anjara, Adrianna Janik, Amy Dunford-Stenger, et al.
PLoS ONE (2023) Vol. 18, Iss. 9, pp. e0291443-e0291443
Open Access | Times Cited: 8
Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models
Janet H. Hsiao, Jeehye An, Veronica Kit Sum Hui, et al.
npj Science of Learning (2022) Vol. 7, Iss. 1
Open Access | Times Cited: 13
Using extended siamese networks to provide decision support in aquaculture operations
Bjørn Magnus Mathisen, Kerstin Bach, Agnar Aamodt
Applied Intelligence (2021) Vol. 51, Iss. 11, pp. 8107-8118
Open Access | Times Cited: 17
Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)
Eoin M. Kenny, E. Ruelle, A. Geoghegan, et al.
Lecture notes in computer science (2019), pp. 172-187
Closed Access | Times Cited: 19
Page 1 - Next Page

Scroll to top