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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:

Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence
Carlos Zednik
Philosophy & Technology (2019) Vol. 34, Iss. 2, pp. 265-288
Closed Access | Times Cited: 258

Showing 1-25 of 258 citing articles:

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 (2023) Vol. 99, pp. 101805-101805
Open Access | Times Cited: 630
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Markus Langer, Daniel Oster, Timo Speith, et al.
Artificial Intelligence (2021) Vol. 296, pp. 103473-103473
Open Access | Times Cited: 400
Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review
Lu Xu, Leslie Sanders, Kay Li, et al.
JMIR Cancer (2021) Vol. 7, Iss. 4, pp. e27850-e27850
Open Access | Times Cited: 336
Transparency and the Black Box Problem: Why We Do Not Trust AI
Warren J. von Eschenbach
Philosophy & Technology (2021) Vol. 34, Iss. 4, pp. 1607-1622
Closed Access | Times Cited: 319
Artificial intelligence, transparency, and public decision-making
Karl de Fine Licht, Jenny de Fine Licht
AI & Society (2020) Vol. 35, Iss. 4, pp. 917-926
Open Access | Times Cited: 189
Understanding and shaping the future of work with self-determination theory
Marylène Gagné, Sharon K. Parker, Mark Griffin, et al.
Nature Reviews Psychology (2022) Vol. 1, Iss. 7, pp. 378-392
Open Access | Times Cited: 171
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Luca Longo, Mario Brčić, Federico Cabitza, et al.
Information Fusion (2024) Vol. 106, pp. 102301-102301
Open Access | Times Cited: 137
Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning
Mijin Kim, Chen Chen, Peng Wang, et al.
Nature Biomedical Engineering (2022) Vol. 6, Iss. 3, pp. 267-275
Open Access | Times Cited: 127
Defining the undefinable: the black box problem in healthcare artificial intelligence
Jordan Joseph Wadden
Journal of Medical Ethics (2021) Vol. 48, Iss. 10, pp. 764-768
Closed Access | Times Cited: 118
The explainability paradox: Challenges for xAI in digital pathology
Theodore Evans, Carl Orge Retzlaff, Christian Geißler, et al.
Future Generation Computer Systems (2022) Vol. 133, pp. 281-296
Open Access | Times Cited: 94
Learning to work with the black box: Pedagogy for a world with artificial intelligence
Margaret Bearman, Rola Ajjawi
British Journal of Educational Technology (2023) Vol. 54, Iss. 5, pp. 1160-1173
Open Access | Times Cited: 86
Smart Health
Yin Yang, Keng Siau, Wen Xie, et al.
Journal of Organizational and End User Computing (2022) Vol. 34, Iss. 1, pp. 1-14
Open Access | Times Cited: 85
Groundwater vulnerability assessment: A review including new statistical and hybrid methods
Nasrin Taghavi, Robert K. Niven, David Paull, et al.
The Science of The Total Environment (2022) Vol. 822, pp. 153486-153486
Open Access | Times Cited: 72
Explainable AI for Bioinformatics: Methods, Tools and Applications
Md. Rezaul Karim, Tanhim Islam, Md Shajalal, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 5
Open Access | Times Cited: 66
The risks of using ChatGPT to obtain common safety-related information and advice
Óscar Oviedo-Trespalacios, Amy E. Peden, Tom Cole‐Hunter, et al.
Safety Science (2023) Vol. 167, pp. 106244-106244
Open Access | Times Cited: 65
Key Science Goals for the Next-Generation Event Horizon Telescope
Michael D. Johnson, Kazunori Akiyama, Lindy Blackburn, et al.
Galaxies (2023) Vol. 11, Iss. 3, pp. 61-61
Open Access | Times Cited: 51
Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
Van Nhanh Nguyen, W. Tarełko, Prabhakar Sharma, et al.
Energy & Fuels (2024) Vol. 38, Iss. 3, pp. 1692-1712
Closed Access | Times Cited: 46
Ethical Considerations in the Use of Artificial Intelligence in Pain Medicine
Marco Cascella, Mohammed Naveed Shariff, Omar Viswanath, et al.
Current Pain and Headache Reports (2025) Vol. 29, Iss. 1
Closed Access | Times Cited: 2
Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach
Sanghee Moon, Hyun-Je Song, Vibhash D. Sharma, et al.
Journal of NeuroEngineering and Rehabilitation (2020) Vol. 17, Iss. 1
Open Access | Times Cited: 92
Reviewing the Need for Explainable Artificial Intelligence (xAI)
Julie Gerlings, Arisa Shollo, Ioanna Constantiou
Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences (2021)
Open Access | Times Cited: 83
Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering
Mohamed Helmy, Derek J. Smith, Kumar Selvarajoo
Metabolic Engineering Communications (2020) Vol. 11, pp. e00149-e00149
Open Access | Times Cited: 81
Exploring machine learning potential for climate change risk assessment
Federica Zennaro, Elisa Furlan, Christian Simeoni, et al.
Earth-Science Reviews (2021) Vol. 220, pp. 103752-103752
Closed Access | Times Cited: 77
Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI
Federico Cabitza, Andrea Campagner, Gianclaudio Malgieri, et al.
Expert Systems with Applications (2022) Vol. 213, pp. 118888-118888
Open Access | Times Cited: 64
How Explainability Contributes to Trust in AI
Andrea Ferrario, Michele Loi
2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 1457-1466
Closed Access | Times Cited: 57
The current and future uses of machine learning in ecosystem service research
Matthew Scowen, Ioannis N. Athanasiadis, James M. Bullock, et al.
The Science of The Total Environment (2021) Vol. 799, pp. 149263-149263
Open Access | Times Cited: 56
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