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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:

A review of possible effects of cognitive biases on interpretation of rule-based machine learning models
Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz
Artificial Intelligence (2021) Vol. 295, pp. 103458-103458
Open Access | Times Cited: 94

Showing 1-25 of 94 citing articles:

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
Andrés Páez
Minds and Machines (2019) Vol. 29, Iss. 3, pp. 441-459
Closed Access | Times Cited: 85
How Cognitive Biases Affect XAI-assisted Decision-making
Astrid Bertrand, Rafik Belloum, James Eagan, et al.
(2022), pp. 78-91
Open Access | Times Cited: 67
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
Charvi Rastogi, Yunfeng Zhang, Dennis Wei, et al.
Proceedings of the ACM on Human-Computer Interaction (2022) Vol. 6, Iss. CSCW1, pp. 1-22
Open Access | Times Cited: 59
Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging
Sergio Sanchez‐Martinez, Óscar Cámara, Gemma Piella, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 8
Open Access | Times Cited: 57
Why do people avoid and postpone the use of voice assistants for transactional purposes? A perspective from decision avoidance theory
Suresh Malodia, Puneet Kaur, Peter Ractham, et al.
Journal of Business Research (2022) Vol. 146, pp. 605-618
Open Access | Times Cited: 43
A qualitatively analyzable two-stage ensemble model based on machine learning for credit risk early warning: Evidence from Chinese manufacturing companies
Lu Wang, Wenyao Zhang
Information Processing & Management (2023) Vol. 60, Iss. 3, pp. 103267-103267
Closed Access | Times Cited: 39
Explainable artificial intelligence in information systems: A review of the status quo and future research directions
Julia Brasse, Hanna Rebecca Broder, Maximilian Förster, et al.
Electronic Markets (2023) Vol. 33, Iss. 1
Open Access | Times Cited: 36
Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields
Zewei Jiang, Shihong Yang, Pete Smith, et al.
Field Crops Research (2023) Vol. 292, pp. 108821-108821
Closed Access | Times Cited: 30
Never tell me the odds: Investigating pro-hoc explanations in medical decision making
Federico Cabitza, Chiara Natali, Lorenzo Famiglini, et al.
Artificial Intelligence in Medicine (2024) Vol. 150, pp. 102819-102819
Open Access | Times Cited: 11
On cognitive preferences and the plausibility of rule-based models
Johannes Fürnkranz, Tomáš Kliegr, Heiko Paulheim
Machine Learning (2019) Vol. 109, Iss. 4, pp. 853-898
Open Access | Times Cited: 65
Can AI be racist? Color‐evasiveness in the application of machine learning to science assessments
Tina Cheuk
Science Education (2021) Vol. 105, Iss. 5, pp. 825-836
Closed Access | Times Cited: 47
Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender
Ludovica Marinucci, Claudia Mazzuca, Aldo Gangemi
AI & Society (2022) Vol. 38, Iss. 2, pp. 747-761
Open Access | Times Cited: 34
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: 17
Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Kamali Sripathi, Rosa A. Moscarella, Matthew M. Steele, et al.
Journal of Mixed Methods Research (2023) Vol. 18, Iss. 1, pp. 48-70
Closed Access | Times Cited: 16
Fairness via Explanation Quality
Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, et al.
(2022), pp. 203-214
Open Access | Times Cited: 26
How AI tools can—and cannot—help organizations become more ethical
David De Cremer, Devesh Narayanan
Frontiers in Artificial Intelligence (2023) Vol. 6
Open Access | Times Cited: 14
Ethics of using artificial intelligence (AI) in veterinary medicine
Simon Coghlan, Thomas P. Quinn
AI & Society (2023) Vol. 39, Iss. 5, pp. 2337-2348
Open Access | Times Cited: 13
Correcting Students' Misconceptions in Physics Using Experiments Designed by ChatGPT
Konstantinos Τ. Kotsis
European Journal of Contemporary Education and E-Learning (2024) Vol. 2, Iss. 2, pp. 83-100
Open Access | Times Cited: 5
Artificial Intelligence and Cognitive Biases: A Viewpoint
Alexander Brem, Giorgia Rivieccio
Journal of Innovation Economics & Management (2024) Vol. N° 44, Iss. 2, pp. 223-231
Open Access | Times Cited: 5
AI and XAI second opinion: the danger of false confirmation in human–AI collaboration
Rikard Rosenbacke, Åsa Melhus, Martin McKee, et al.
Journal of Medical Ethics (2024), pp. jme-110074
Closed Access | Times Cited: 5
Explainable Artificial Intelligence in Data Science
Joaquín Borrego-Dí­az, Juan Galán-Páez
Minds and Machines (2022) Vol. 32, Iss. 3, pp. 485-531
Open Access | Times Cited: 19
Human–Artificial Intelligence Collaboration in Prediction: A Field Experiment in the Retail Industry
Elena Revilla, María Jesús Sáenz, Matthias Seifert, et al.
Journal of Management Information Systems (2023) Vol. 40, Iss. 4, pp. 1071-1098
Closed Access | Times Cited: 11
A Cognitive Load Theory (CLT) Analysis of Machine Learning Explainability, Transparency, Interpretability, and Shared Interpretability
Stephen Fox, Vítor Fortes Rey
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 3, pp. 1494-1509
Open Access | Times Cited: 4
Navigating the data frontier in science assessment: Advancing data augmentation strategies for machine learning applications with generative artificial intelligence
Paul P. Martin, Nicole Graulich
Computers and Education Artificial Intelligence (2024) Vol. 7, pp. 100265-100265
Open Access | Times Cited: 4
How Explainable Artificial Intelligence Can Increase or Decrease Clinicians’ Trust in AI Applications in Health Care: Systematic Review
Rikard Rosenbacke, Åsa Melhus, Martin McKee, et al.
JMIR AI (2024) Vol. 3, pp. e53207-e53207
Open Access | Times Cited: 4
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