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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:

Mitigating Gender Bias in Natural Language Processing: Literature Review
Tony Sun, Andrew Gaut, Shirlyn Tang, et al.
(2019)
Open Access | Times Cited: 417

Showing 1-25 of 417 citing articles:

Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé, et al.
(2020)
Open Access | Times Cited: 678
Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Davide Cirillo, Silvina Catuara‐Solarz, Czuee Morey, et al.
npj Digital Medicine (2020) Vol. 3, Iss. 1
Open Access | Times Cited: 388
Formalizing Trust in Artificial Intelligence
Alon Jacovi, Ana Marasović, Tim Miller, et al.
(2021), pp. 624-635
Closed Access | Times Cited: 296
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
Deven Santosh Shah, H. Andrew Schwartz, Dirk Hovy
(2020)
Open Access | Times Cited: 221
Fairness in Machine Learning: A Survey
Simon Caton, Christian Haas
ACM Computing Surveys (2023) Vol. 56, Iss. 7, pp. 1-38
Open Access | Times Cited: 208
Five sources of bias in natural language processing
Dirk Hovy, Shrimai Prabhumoye
Language and Linguistics Compass (2021) Vol. 15, Iss. 8
Open Access | Times Cited: 176
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Emily Dinan, Angela Fan, Adina Williams, et al.
(2020)
Open Access | Times Cited: 144
Should ChatGPT be biased? Challenges and risks of bias in large language models
Emilio Ferrara
First Monday (2023)
Open Access | Times Cited: 132
The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals
Nadine Bachmann, Shailesh Tripathi, Brunner Manuel, et al.
Sustainability (2022) Vol. 14, Iss. 5, pp. 2497-2497
Open Access | Times Cited: 125
Biases in Large Language Models: Origins, Inventory, and Discussion
Roberto Navigli, Simone Conia, Björn Roß
Journal of Data and Information Quality (2023) Vol. 15, Iss. 2, pp. 1-21
Open Access | Times Cited: 125
Gender bias and stereotypes in Large Language Models
Hadas Kotek, Rikker Dockum, David Sun
(2023), pp. 12-24
Open Access | Times Cited: 117
Gender Bias in Machine Translation
Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, et al.
Transactions of the Association for Computational Linguistics (2021) Vol. 9, pp. 845-874
Open Access | Times Cited: 115
BOLD
Jwala Dhamala, Tony Sun, Varun Kumar, et al.
(2021), pp. 862-872
Open Access | Times Cited: 109
Pre-trained Language Models in Biomedical Domain: A Systematic Survey
Benyou Wang, Qianqian Xie, Jiahuan Pei, et al.
ACM Computing Surveys (2023) Vol. 56, Iss. 3, pp. 1-52
Open Access | Times Cited: 86
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort, Zhenpeng Chen, Jie M. Zhang, et al.
ACM Journal on Responsible Computing (2023) Vol. 1, Iss. 2, pp. 1-52
Open Access | Times Cited: 80
Ethical implications of text generation in the age of artificial intelligence
Laura Illia, Elanor Colleoni, Stelios C. Zyglidopoulos
Business Ethics the Environment & Responsibility (2022) Vol. 32, Iss. 1, pp. 201-210
Open Access | Times Cited: 78
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
Shangbin Feng, Chan Young Park, Yuhan Liu, et al.
(2023)
Open Access | Times Cited: 67
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models
Patrick Schramowski, Manuel Brack, Björn Deiseroth, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Open Access | Times Cited: 64
Handling Bias in Toxic Speech Detection: A Survey
Tanmay Garg, Sarah Masud, Tharun Suresh, et al.
ACM Computing Surveys (2023) Vol. 55, Iss. 13s, pp. 1-32
Open Access | Times Cited: 46
Bias of AI-generated content: an examination of news produced by large language models
Xiao Fang, Shangkun Che, Minjia Mao, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 44
Representation Bias in Data: A Survey on Identification and Resolution Techniques
Nima Shahbazi, Lin Yin, Abolfazl Asudeh, et al.
ACM Computing Surveys (2023) Vol. 55, Iss. 13s, pp. 1-39
Open Access | Times Cited: 42
Re-think Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities and Challenges
Abdulaziz Aldoseri, Khalifa N. Al‐Khalifa, A.M.S. Hamouda
(2023)
Open Access | Times Cited: 42
Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
Shengbang Tong, Zhuang Liu, Yuexiang Zhai, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024), pp. 9568-9578
Closed Access | Times Cited: 26
Addressing the antibody germline bias and its effect on language models for improved antibody design
Tobias Hegelund Olsen, Iain H. Moal, Charlotte M. Deane
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 18
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings
Rishi Bommasani, Kelly Davis, Claire Cardie
(2020)
Open Access | Times Cited: 135
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