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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:

Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou, et al.
(2021), pp. 1-16
Open Access | Times Cited: 363

Showing 1-25 of 363 citing articles:

To Trust or to Think
Zana Buçinca, Maja Barbara Malaya, Krzysztof Z. Gajos
Proceedings of the ACM on Human-Computer Interaction (2021) Vol. 5, Iss. CSCW1, pp. 1-21
Open Access | Times Cited: 294
Interpretable machine learning
Valerie Chen, Jeffrey Li, Joon Sik Kim, et al.
Communications of the ACM (2022) Vol. 65, Iss. 8, pp. 43-50
Open Access | Times Cited: 207
AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts
Tongshuang Wu, Michael Terry, Carrie J. Cai
CHI Conference on Human Factors in Computing Systems (2022), pp. 1-22
Open Access | Times Cited: 198
Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
Xinru Wang, Ming Yin
(2021), pp. 318-328
Open Access | Times Cited: 179
Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support
Ashish Sharma, Inna Wanyin Lin, Adam S. Miner, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 1, pp. 46-57
Closed Access | Times Cited: 174
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: 135
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Tongshuang Wu, Marco Túlio Ribeiro, Jeffrey Heer, et al.
(2021)
Open Access | Times Cited: 126
Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
Haomin Chen, Catalina Gómez, Chien‐Ming Huang, et al.
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 122
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna, Tessa Han, Alex Gu, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 107
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng, et al.
CHI Conference on Human Factors in Computing Systems (2022)
Open Access | Times Cited: 81
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Sunnie Kim, Elizabeth Anne Watkins, Olga Russakovsky, et al.
(2023), pp. 1-17
Open Access | Times Cited: 80
The flaws of policies requiring human oversight of government algorithms
Ben Green
Computer Law & Security Review (2022) Vol. 45, pp. 105681-105681
Open Access | Times Cited: 74
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Vivian Lai, Samuel Carton, Rajat Bhatnagar, et al.
CHI Conference on Human Factors in Computing Systems (2022)
Open Access | Times Cited: 69
What is Human-Centered about Human-Centered AI? A Map of the Research Landscape
Tara Capel, Margot Brereton
(2023), pp. 1-23
Open Access | Times Cited: 63
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: 62
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations
Max Schemmer, Niklas Kuehl, Carina Benz, et al.
(2023), pp. 410-422
Open Access | Times Cited: 54
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, et al.
Proceedings of the ACM on Human-Computer Interaction (2023) Vol. 7, Iss. CSCW2, pp. 1-32
Open Access | Times Cited: 53
Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis
Federico Cabitza, Andrea Campagner, Luca Ronzio, et al.
Artificial Intelligence in Medicine (2023) Vol. 138, pp. 102506-102506
Open Access | Times Cited: 48
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
Xiaojuan Ma, Ying Lei, Xinru Wang, et al.
(2023), pp. 1-19
Open Access | Times Cited: 47
RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
Yunlong Wang, Shuyuan Shen, Brian Y. Lim
(2023), pp. 1-29
Open Access | Times Cited: 46
Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making
Kori Inkpen, Shreya Chappidi, Keri Mallari, et al.
ACM Transactions on Computer-Human Interaction (2023) Vol. 30, Iss. 5, pp. 1-29
Open Access | Times Cited: 45
Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis
Francisco Maria Calisto, João Paulo Fernandes, Margarida Morais, et al.
(2023), pp. 1-20
Open Access | Times Cited: 44
Towards Faithful Model Explanation in NLP: A Survey
Qing Lyu, Marianna Apidianaki, Chris Callison-Burch
Computational Linguistics (2024) Vol. 50, Iss. 2, pp. 657-723
Open Access | Times Cited: 19
When combinations of humans and AI are useful: A systematic review and meta-analysis
Michelle Vaccaro, Abdullah Almaatouq, Thomas W. Malone
Nature Human Behaviour (2024)
Open Access | Times Cited: 15
What large language models know and what people think they know
Mark Steyvers, Heliodoro Tejeda, Aakriti Kumar, et al.
Nature Machine Intelligence (2025)
Open Access | Times Cited: 2
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