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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:

The Importance of Understanding Deep Learning
Tim Räz, Claus Beisbart
Erkenntnis (2022) Vol. 89, Iss. 5, pp. 1823-1840
Open Access | Times Cited: 25

Showing 25 citing articles:

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
Explainable AI and Causal Understanding: Counterfactual Approaches Considered
Sam Baron
Minds and Machines (2023) Vol. 33, Iss. 2, pp. 347-377
Open Access | Times Cited: 28
ML interpretability: Simple isn't easy
Tim Räz
Studies in History and Philosophy of Science Part A (2024) Vol. 103, pp. 159-167
Open Access | Times Cited: 8
Exploring Insights in Biomass and Waste Gasification via Ensemble Machine Learning Models and Interpretability Techniques
Ocident Bongomin, Charles Nzila, Josphat Igadwa Mwasiagi, et al.
International Journal of Energy Research (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 7
Reliability in Machine Learning
Thomas Grote, Konstantin Genin, Emily Sullivan
Philosophy Compass (2024) Vol. 19, Iss. 5
Open Access | Times Cited: 6
Machine learning for the physics of climate
Annalisa Bracco, Julien Brajard, Henk A. Dijkstra, et al.
Nature Reviews Physics (2024) Vol. 7, Iss. 1, pp. 6-20
Closed Access | Times Cited: 4
The Epistemic Cost of Opacity: How the Use of Artificial Intelligence Undermines the Knowledge of Medical Doctors in High-Stakes Contexts
Eva Schmidt, Paul Martin Putora, Rianne Fijten
Philosophy & Technology (2025) Vol. 38, Iss. 1
Open Access
Deep Learning Innovations in the Detection of Lung Cancer: Advances, Trends, and Open Challenges
Helena Liz, Áurea Anguera de Sojo-Hernández, Sergio D’Antonio Maceiras, et al.
Cognitive Computation (2025) Vol. 17, Iss. 2
Open Access
Classification Feature Optimization for Global Wetlands Mapping
Xin Yan, Zhenguo Niu
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 8058-8072
Open Access | Times Cited: 3
Machine learning and the quest for objectivity in climate model parameterization
Julie Jebeile, Vincent Lam, Mason Majszak, et al.
Climatic Change (2023) Vol. 176, Iss. 8
Open Access | Times Cited: 7
Instruments, agents, and artificial intelligence: novel epistemic categories of reliability
Eamon Duede
Synthese (2022) Vol. 200, Iss. 6
Open Access | Times Cited: 11
Predicting and explaining with machine learning models: Social science as a touchstone
Oliver Buchholz, Thomas Grote
Studies in History and Philosophy of Science Part A (2023) Vol. 102, pp. 60-69
Closed Access | Times Cited: 4
The Explanatory Role of Machine Learning in Molecular Biology
Fridolin Groß
Erkenntnis (2024)
Closed Access | Times Cited: 1
Research Advances in Deep Learning for Image Semantic Segmentation Techniques
Zhiguo Xiao, Tengfei Chai, Nianfeng Li, et al.
IEEE Access (2024) Vol. 12, pp. 175715-175741
Open Access | Times Cited: 1
Fuzzy Neural Logic Reasoning for Robust Classification
Guo Lin, Yongfeng Zhang
ACM Transactions on Knowledge Discovery from Data (2024)
Open Access | Times Cited: 1
Methods for identifying emergent concepts in deep neural networks
Tim Räz
Patterns (2023) Vol. 4, Iss. 6, pp. 100761-100761
Open Access | Times Cited: 3
Do Machine Learning Models Represent Their Targets?
Emily Sullivan
Philosophy of Science (2023) Vol. 91, Iss. 5, pp. 1445-1455
Open Access | Times Cited: 3
Causal scientific explanations from machine learning
Stefan Buijsman
Synthese (2023) Vol. 202, Iss. 6
Closed Access | Times Cited: 3
Epistemic implications of machine learning models in science
Stefan Buijsman, Juan M. Durán
Routledge eBooks (2024), pp. 456-468
Open Access
Transparency and Explainability for Public Policy
Kate Vredenburgh
LSE Public Policy Review (2024) Vol. 3, Iss. 3
Open Access
NER Explainability Framework: Utilizing LIME to Enhance Clarity and Robustness in Named Entity Recognition
Morten Grundetjern, Per‐Arne Andersen, Morten Goodwin, et al.
Lecture notes in computer science (2024), pp. 3-15
Closed Access
Deep Learning in Science: Is there a Reason for (Philosophical) Pessimism?
Martin Justin
Interdisciplinary Description of Complex Systems (2024) Vol. 22, Iss. 1, pp. 59-70
Open Access
Basic Knowledge of AI for Clinicians
Marco Cascella
(2024), pp. 5-24
Closed Access
ML Interpretability: Simple Isn't Easy
Tim Räz
arXiv (Cornell University) (2022)
Open Access | Times Cited: 1
Emergence of Concepts in DNNs?
Tim Räz
arXiv (Cornell University) (2022)
Open Access
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