Performance and biases of Large Language Models in public opinion simulation

Qu, Yao; Wang, Jue

doi:10.1057/s41599-024-03609-x

Humanit Soc Sci Commun

2024

DOI: 10.1057/s41599-024-03609-x

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Performance and biases of Large Language Models in public opinion simulation

Yao Qu,

Jue Wang

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2024

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Reducing bias in healthcare artificial intelligence: A white paper

Sun,

Harris

2024

Health Informatics J

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Objective: Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. Methods: At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. Results: This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Conclusions: Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.

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Reducing bias in healthcare artificial intelligence: A white paper

Sun,

Harris

2024

Health Informatics J

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show abstract

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Performance and biases of Large Language Models in public opinion simulation

Cited by 1 publication

References 24 publications

Reducing bias in healthcare artificial intelligence: A white paper

Reducing bias in healthcare artificial intelligence: A white paper

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