Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency (2025.emnlp-main)
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Svetlana Maslenkova, Clement Christophe, Marco AF Pimentel, Tathagata Raha, Muhammad Umar Salman, Ahmed Al Mahrooqi, Avani Gupta, Shadab Khan, Ronnie Rajan, Praveenkumar Kanithi
| Challenge: | Current dataset curation and bias assessment practices lack transparency . current approaches lack a thorough understanding of how data characteristics influence model behavior . |
| Approach: | They propose a comprehensive bias evaluation framework that integrates general benchmarks with a healthcare-specific methodology to probe for biases in a sensitive healthcare context. |
| Outcome: | The proposed approach to bias evaluation leverages established benchmarks and a healthcare-specific methodology. |
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| Challenge: | Pretrained language models (PLMs) propagate social stigmas and stereotypes, a critical concern given their widespread use. |
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How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making? (2025.naacl-long)
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| Challenge: | Recent studies have shown that LLMs exhibit social biases inherited from training data. |
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Can LLMs Replace Clinical Doctors? Exploring Bias in Disease Diagnosis by Large Language Models (2024.findings-emnlp)
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| Challenge: | a new study examines the bias of disease prediction in large language models . the model biases are prevalent across gender, age range and disease judgment behaviors . |
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A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare (2025.findings-emnlp)
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ROBBIE: Robust Bias Evaluation of Large Generative Language Models (2023.emnlp-main)
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David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith
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| Challenge: | Existing large language models (LLMs) are not effective in solving real-world healthcare tasks, but they are able to provide demographic information and provide biased health predictions. |
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Md. Faiyaz Abdullah Sayeedi, Subhey Sadi Rahman, Md. Mahbub Alam, Md. Adnanul Islam, Jannatul Ferdous Deepti, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
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Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth
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The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have emerged as the new recommendation engines, surpassing traditional methods in both capability and scope, particularly in code generation. |
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