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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:

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Christoph Molnar, Gunnar König, Julia Herbinger, et al.
Lecture notes in computer science (2022), pp. 39-68
Open Access | Times Cited: 93

Showing 26-50 of 93 citing articles:

Enhancing Predictive Accuracy in Used Car Pricing Models: A Comparative Analysis of Regression Techniques
Ved Prakash Chaubey, Devang Sareen, Chanda Harshita, et al.
Communications in computer and information science (2025), pp. 24-35
Closed Access
LSTM Ensemble Learning Model with Additive Gaussian Process Priors
Zhuo Sun, Yishu Wang, Hanlin Yin
Highlights in Science Engineering and Technology (2025) Vol. 136, pp. 316-327
Open Access
Feature Similarity Group-Class Activation Mapping (FSG-CAM): Clarity in deep learning models and Enhancement of visual explanations
Dan Wang, Yuze Xia, Witold Pedrycz, et al.
Expert Systems with Applications (2025), pp. 127553-127553
Closed Access
New analytical approaches to life course epidemiology
Claudia Börnhorst, Marvin N. Wright, Vanessa Didelez
Oxford University Press eBooks (2025), pp. 81-100
Closed Access
Decoding vital variables in predicting different phases of suicide among young adults with childhood sexual abuse: a machine learning approach
Wenbang Niu, Yi Feng, Jiaqi Li, et al.
Translational Psychiatry (2025) Vol. 15, Iss. 1
Open Access
Prediction of defensive success in elite soccer using machine learning - Tactical analysis of defensive play using tracking data and explainable AI
Leander Forcher, Tobias Beckmann, Oliver Wohak, et al.
Science and Medicine in Football (2023), pp. 1-16
Closed Access | Times Cited: 10
Interpretable artificial intelligence in radiology and radiation oncology
Sunan Cui, Alberto Traverso, Dipesh Niraula, et al.
British Journal of Radiology (2023) Vol. 96, Iss. 1150
Open Access | Times Cited: 10
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview
Katharina Beckh, Sebastian Müller, Matthias Jakobs, et al.
(2023), pp. 450-463
Closed Access | Times Cited: 9
Addressing diversity in hiring procedures: a generative adversarial network approach
Tales Marra, Emeric Kubiak
AI and Ethics (2024)
Closed Access | Times Cited: 3
Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification
Alexander Brenning
Machine Learning (2023) Vol. 112, Iss. 9, pp. 3455-3471
Open Access | Times Cited: 8
A general steganographic framework for neural network models
Ziyun Yang, Zichi Wang, Xinpeng Zhang
Information Sciences (2023) Vol. 643, pp. 119250-119250
Closed Access | Times Cited: 8
Dear XAI Community, We Need to Talk!
Timo Freiesleben, Gunnar König
Communications in computer and information science (2023), pp. 48-65
Closed Access | Times Cited: 7
The promise of automated machine learning for the genetic analysis of complex traits
Elisabetta Manduchi, Joseph D. Romano, Jason H. Moore
Human Genetics (2021) Vol. 141, Iss. 9, pp. 1529-1544
Open Access | Times Cited: 16
Marginal effects for non-linear prediction functions
Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, et al.
Data Mining and Knowledge Discovery (2024) Vol. 38, Iss. 5, pp. 2997-3042
Open Access | Times Cited: 2
Machine Learning Approach towards Quality Assurance, Challenges and Possible Strategies in Laboratory Medicine
Qurrat ul Ain, Rameesha Nazir, Aleeza Nawaz, et al.
Journal of Clinical and Translational Pathology (2024) Vol. 4, Iss. 2, pp. 76-87
Open Access | Times Cited: 2
Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
Timo Freiesleben, Gunnar König, Christoph Molnar, et al.
Minds and Machines (2024) Vol. 34, Iss. 3
Open Access | Times Cited: 2
Beyond model interpretability: socio-structural explanations in machine learning
Andrew Smart, Atoosa Kasirzadeh
AI & Society (2024)
Open Access | Times Cited: 2
Commentary on explainable artificial intelligence methods: SHAP and LIME
Ahmed Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 6
Predicting T Cell Receptor Functionality against Mutant Epitopes
Emilio Dorigatti, Felix Drost, Adrian Straub, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 6
Can local explanation techniques explain linear additive models?
Amir Hossein Akhavan Rahnama, Judith Bütepage, Pierre Geurts, et al.
Data Mining and Knowledge Discovery (2023) Vol. 38, Iss. 1, pp. 237-280
Open Access | Times Cited: 6
Preventing deception with explanation methods using focused sampling
Domen Vreš, Marko Robnik–Šikonja
Data Mining and Knowledge Discovery (2022) Vol. 38, Iss. 5, pp. 3262-3307
Closed Access | Times Cited: 8
Can You Solve This on the First Try? – Understanding Exercise Field Performance in an Intelligent Tutoring System
Hannah Deininger, Rosa Lavelle-Hill, Cora Parrisius, et al.
Lecture notes in computer science (2023), pp. 565-576
Closed Access | Times Cited: 4
Initialization Noise in Image Gradients and Saliency Maps
Ann-Christin Woerl, Jan Disselhoff, Michael Wand
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023) Vol. abs 1706 3825, pp. 1766-1775
Closed Access | Times Cited: 4
Genetic algorithm-based hybrid deep learning model for explainable Alzheimer’s disease prediction using temporal multimodal cognitive data
Hager Saleh, Nora El-Rashidy, Mohamed Abd Elaziz, et al.
International Journal of Data Science and Analytics (2024)
Open Access | Times Cited: 1
The Use of Machine Learning Methods in Political Science: An In-Depth Literature Review
Jef de Slegte, Filip Van Droogenbroeck, Bram Spruyt, et al.
Political Studies Review (2024)
Closed Access | Times Cited: 1

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