โก Quick Summary
This study explores the implicit biases present in large language models (LLMs) regarding global public health attitudes, highlighting significant challenges to health equity. The findings reveal that data accessibility and public opinion consensus significantly influence the representational fidelity of these models.
๐ Key Details
- ๐ Models Analyzed: Gemini 2.5 Pro, GPT-5, DeepSeek-V3, Qwen 3
- ๐ Focus Areas: Representation of health attitudes across diverse nations and demographics
- โ๏ธ Framework Proposed: Three-dimensional framework including Data Resources, Opinion Distribution, and Prompt Language
- ๐ Key Metrics: Native language performance and cross-lingual consistency
๐ Key Takeaways
- ๐ Data Accessibility is crucial for LLMs to accurately represent health attitudes.
- ๐ค Consensus in Public Health Opinion leads to models favoring dominant viewpoints.
- ๐ฃ๏ธ Native Language Association shows that models perform better in their native languages.
- ๐ Multilingual Models like GPT-5 and Qwen 3 demonstrate greater consistency across languages.
- ๐ Implications for Health Equity are profound, necessitating careful consideration in AI applications.
- ๐ Analytical Pathway provided for understanding representational biases in LLMs.
๐ Background
As digital health tools evolve, large language models (LLMs) are increasingly utilized to reflect public health perspectives. However, these models can inadvertently embody systematic biases derived from the data they are trained on. Understanding these biases is essential for promoting health equity in an era where AI plays a pivotal role in healthcare decision-making.
๐๏ธ Study
This study systematically analyzed prominent LLMs from the United States and China, focusing on their ability to represent health attitudes across various demographics. By employing a three-dimensional framework, the researchers aimed to uncover the underlying biases that may affect the models’ outputs and their implications for public health.
๐ Results
The analysis revealed that the accessibility of data resources is a primary factor influencing the representational fidelity of LLMs. Additionally, a greater consensus in public health opinion was found to correlate with the models’ tendency to replicate dominant viewpoints. Notably, models like Gemini 2.5 Pro and DeepSeek-V3 performed better when prompted in their native languages, while multilingual models such as GPT-5 and Qwen 3 exhibited greater cross-lingual consistency.
๐ Impact and Implications
The findings of this study underscore the importance of addressing implicit biases in LLMs to enhance health equity. As these models become integral to public health discourse, understanding their limitations and biases is crucial for ensuring that they serve diverse populations effectively. This research paves the way for future investigations into the ethical deployment of AI in healthcare.
๐ฎ Conclusion
This study highlights the critical need for awareness of implicit biases in digital health tools, particularly large language models. By recognizing the factors that influence their performance, we can work towards creating more equitable AI systems that better reflect the diverse health attitudes of global populations. Continued research in this area is essential for fostering a more inclusive future in healthcare technology.
๐ฌ Your comments
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Implicit bias in digital health: systematic biases in large language models’ representation of global public health attitudes and challenges to health equity.
Abstract
INTRODUCTION: As emerging instruments in digital health, large language models (LLMs) assimilate values and attitudes from human-generated data, thereby possessing the latent capacity to reflect public health perspectives. This study investigates into the representational biases of LLMs through the lens of health equity. We propose and empirically validate a three-dimensional explanatory framework encompassing Data Resources, Opinion Distribution, and Prompt Language, positing that prompts are not just communicative media but critical conduits that embed cultural context.
METHODS: Utilizing a selection of prominent LLMs from the United States and China-namely Gemini 2.5 Pro, GPT-5, DeepSeek-V3, and Qwen 3. We conduct a systematic empirical analysis of their performance in representing health attitudes across diverse nations and demographic strata.
RESULTS: Our findings demonstrate that: first, the accessibility of data resources is a primary determinant of an LLM’s representational fidelity for internet users and nations with high internet penetration. Second, a greater consensus in public health opinion correlates with an increased propensity for the models to replicate the dominant viewpoint. Third, a significant “native language association” is observed, wherein Gemini 2.5 Pro and DeepSeek-V3 exhibit superior performance when prompted in their respective native languages. Conversely, models with enhanced multilingual proficiencies, such as GPT-5.0 and Qwen 3, display greater cross-lingual consistency.
DISCUSSION: This paper not only quantifies the degree to which these leading LLMs reflect public health attitudes but also furnishes a robust analytical pathway for dissecting the underlying mechanisms of their representational biases. These findings bear profound implications for the advancement of health equity in the artificial intelligence era.
Author: [‘Gao Y’, ‘Feng Y’, ‘Jahng SG’]
Journal: Front Public Health
Citation: Gao Y, et al. Implicit bias in digital health: systematic biases in large language models’ representation of global public health attitudes and challenges to health equity. Implicit bias in digital health: systematic biases in large language models’ representation of global public health attitudes and challenges to health equity. 2025; 13:1705082. doi: 10.3389/fpubh.2025.1705082