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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 Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan, et al.
2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) (2018), pp. 80-89
Open Access | Times Cited: 1772

Showing 1-25 of 1772 citing articles:

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, et al.
Information Fusion (2019) Vol. 58, pp. 82-115
Open Access | Times Cited: 5893
Explainable AI: A Review of Machine Learning Interpretability Methods
Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis
Entropy (2020) Vol. 23, Iss. 1, pp. 18-18
Open Access | Times Cited: 1873
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Erico Tjoa, Cuntai Guan
IEEE Transactions on Neural Networks and Learning Systems (2020) Vol. 32, Iss. 11, pp. 4793-4813
Open Access | Times Cited: 1410
Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo V. Carvalho, Eduardo M. Pereira, Jaime S. Cardoso
Electronics (2019) Vol. 8, Iss. 8, pp. 832-832
Open Access | Times Cited: 1341
Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte, et al.
IEEE Access (2020) Vol. 8, pp. 42200-42216
Open Access | Times Cited: 752
Drug discovery with explainable artificial intelligence
José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
Nature Machine Intelligence (2020) Vol. 2, Iss. 10, pp. 573-584
Open Access | Times Cited: 720
A Survey on Neural Network Interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis, et al.
IEEE Transactions on Emerging Topics in Computational Intelligence (2021) Vol. 5, Iss. 5, pp. 726-742
Open Access | Times Cited: 582
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
Q. Vera Liao, Daniel M. Gruen, Sarah Miller
(2020), pp. 1-15
Open Access | Times Cited: 562
Explainable artificial intelligence: an analytical review
Plamen Angelov, Eduardo Soares, Richard Jiang, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2021) Vol. 11, Iss. 5
Open Access | Times Cited: 511
Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, et al.
(2020), pp. 648-657
Open Access | Times Cited: 496
The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies
Aniek F. Markus, Jan A. Kors, Peter R. Rijnbeek
Journal of Biomedical Informatics (2020) Vol. 113, pp. 103655-103655
Open Access | Times Cited: 479
Captum: A unified and generic model interpretability library for PyTorch
Narine Kokhlikyan, Vivek Miglani, Miguel Vargas Martín, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 443
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Monika A. Myszczynska, Poojitha N. Ojamies, Alix M.B. Lacoste, et al.
Nature Reviews Neurology (2020) Vol. 16, Iss. 8, pp. 440-456
Closed Access | Times Cited: 442
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Sina Mohseni, Niloofar Zarei, Eric D. Ragan
ACM Transactions on Interactive Intelligent Systems (2021) Vol. 11, Iss. 3-4, pp. 1-45
Open Access | Times Cited: 419
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics
Jianlong Zhou, Amir H. Gandomi, Fang Chen, et al.
Electronics (2021) Vol. 10, Iss. 5, pp. 593-593
Open Access | Times Cited: 409
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, et al.
(2020), pp. 1-14
Closed Access | Times Cited: 402
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: 401
Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT
Pawan Budhwar, Soumyadeb Chowdhury, Geoffrey Wood, et al.
Human Resource Management Journal (2023) Vol. 33, Iss. 3, pp. 606-659
Open Access | Times Cited: 388
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: 387
Interpretable Machine Learning
Valerie Chen, Jeffrey Li, Joon Sik Kim, et al.
Queue (2021) Vol. 19, Iss. 6, pp. 28-56
Open Access | Times Cited: 387
Interpretability of machine learning‐based prediction models in healthcare
Gregor Štiglic, Primož Kocbek, Nino Fijačko, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2020) Vol. 10, Iss. 5
Open Access | Times Cited: 376
On Interpretability of Artificial Neural Networks: A Survey
Fenglei Fan, Jinjun Xiong, Mengzhou Li, et al.
IEEE Transactions on Radiation and Plasma Medical Sciences (2021) Vol. 5, Iss. 6, pp. 741-760
Open Access | Times Cited: 362
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Khan Muhammad, Amin Ullah, Jaime Lloret, et al.
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 22, Iss. 7, pp. 4316-4336
Open Access | Times Cited: 361
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
Alon Jacovi, Yoav Goldberg
(2020)
Open Access | Times Cited: 323
The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies
Alexandre Blanco-González, Alfonso Cabezón, Alejandro Seco-González, et al.
Pharmaceuticals (2023) Vol. 16, Iss. 6, pp. 891-891
Open Access | Times Cited: 307
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