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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:

It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy
Andrew Bell, Ian René Solano-Kamaiko, Oded Nov, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 248-266
Open Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

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: 74
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK
Luca Nannini, Agathe Balayn, Adam Leon Smith
2022 ACM Conference on Fairness, Accountability, and Transparency (2023), pp. 1198-1212
Open Access | Times Cited: 27
In search of verifiability: Explanations rarely enable complementary performance in AI‐advised decision making
Raymond Fok, Daniel S. Weld
AI Magazine (2024) Vol. 45, Iss. 3, pp. 317-332
Open Access | Times Cited: 16
Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making
Sara Salimzadeh, Gaole He, Ujwal Gadiraju
(2024), pp. 1-17
Open Access | Times Cited: 12
Exploring the Intersection of Machine Learning and Big Data: A Survey
Ηλίας Δρίτσας, Μαρία Τρίγκα
Machine Learning and Knowledge Extraction (2025) Vol. 7, Iss. 1, pp. 13-13
Open Access | Times Cited: 1
Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance
Andrew Bell, Oded Nov, Julia Stoyanovich
Data & Policy (2023) Vol. 5
Open Access | Times Cited: 21
Explorable Explainable AI: Improving AI Understanding for Community Health Workers in India
Ian René Solano-Kamaiko, Dibyendu Mishra, Nicola Dell, et al.
(2024), pp. 1-21
Open Access | Times Cited: 8
Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification
Francesco Prinzi, Alessia Angela Maria Orlando, Salvatore Gaglio, et al.
Deleted Journal (2024) Vol. 37, Iss. 3, pp. 1038-1053
Open Access | Times Cited: 7
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks
Kacper Sokol, Julia E. Vogt
(2024), pp. 1-8
Open Access | Times Cited: 7
Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)
Barnaby Crook, Maximilian Schlüter, Timo Speith
(2023), pp. 316-324
Open Access | Times Cited: 15
Comparing code-free and bespoke deep learning approaches in ophthalmology
Carolyn Yu Tung Wong, Ciara O’Byrne, Priyal Taribagil, et al.
Graefe s Archive for Clinical and Experimental Ophthalmology (2024) Vol. 262, Iss. 9, pp. 2785-2798
Open Access | Times Cited: 5
Quantifying polarization in online political discourse
Pau Muñoz, Alejandro Bellogín, Raúl Barba-Rojas, et al.
EPJ Data Science (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 4
Review of Stuck Pipe Prediction Methods and Future Directions
Abraham C. Montes, Pradeepkumar Ashok, Eric van Oort
SPE Journal (2025), pp. 1-30
Closed Access
What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach
Elisabeth Hildt
Bioengineering (2025) Vol. 12, Iss. 4, pp. 375-375
Open Access
Applying Random Forests in Federated Learning: A Synthesis of Aggregation Techniques
Mattis Bodynek, Florian Leiser, Scott Thiebes, et al.
Lecture notes in information systems and organisation (2025), pp. 399-415
Closed Access
Real-world data mining meets clinical practice: Research challenges and perspective
Federica Mandreoli, Davide Ferrari, Veronica Guidetti, et al.
Frontiers in Big Data (2022) Vol. 5
Open Access | Times Cited: 15
Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation
Shaheim Ogbomo-Harmitt, Marica Muffoletto, Aya Mutaz Zeidan, et al.
Frontiers in Physiology (2023) Vol. 14
Open Access | Times Cited: 9
What we owe to decision-subjects: beyond transparency and explanation in automated decision-making
David Gray Grant, Jeff Behrends, John Basl
Philosophical Studies (2023)
Open Access | Times Cited: 9
eXplainable AI with GPT4 for story analysis and generation: A novel framework for diachronic sentiment analysis
Jon Chun, Katherine Elkins
International Journal of Digital Humanities (2023) Vol. 5, Iss. 2-3, pp. 507-532
Closed Access | Times Cited: 9
Explainable Artificial Intelligence in Hydrology: Interpreting Black-Box Snowmelt-Driven Streamflow Predictions in an Arid Andean Basin of North-Central Chile
Jorge Núñez, Catalina B. Cortés, Marjorie A. Yáñez
Water (2023) Vol. 15, Iss. 19, pp. 3369-3369
Open Access | Times Cited: 8
Machine learning in bail decisions and judges’ trustworthiness
Alexis Morin-Martel
AI & Society (2023) Vol. 39, Iss. 4, pp. 2033-2044
Open Access | Times Cited: 7
Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanations
Yueqing Xuan, Edward Small, Kacper Sokol, et al.
International Journal of Human-Computer Studies (2024) Vol. 193, pp. 103376-103376
Open Access | Times Cited: 2
On the Impact of Explanations on Understanding of Algorithmic Decision-Making
Timothée Schmude, Laura Koesten, Torsten Möller, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2023), pp. 959-970
Open Access | Times Cited: 6
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
Fuseini Mumuni, Alhassan Mumuni
Cognitive Systems Research (2023) Vol. 84, pp. 101188-101188
Open Access | Times Cited: 6
Comparing machine-learning models of different levels of complexity for crop protection: A look into the complexity-accuracy tradeoff
Olivier Gauriau, Luis Galárraga, François Brun, et al.
Smart Agricultural Technology (2023) Vol. 7, pp. 100380-100380
Open Access | Times Cited: 6
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