Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health
arXiv:2603.09416v1 Announce Type: new Abstract: Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks, but they often propagate biases embedded in their training data, which is potentially impactful in sensitive domains like healthcare. While existing benchmarks evaluate biases related to individual social determinants of health (SDoH) such as gender or ethnicity, they often overlook interactions between these factors and lack context-specific assessments. This study investigates bias in LLMs by probing the relationships between gender and other SDoH in French patient records. Through a series of experiments, we found that embedded stereotypes can be probed using SDoH input and that LLMs rely on embedded stereotypes to make gendered decisions, suggesting that evaluating interactions among SDoH factors could usefully complement existing approaches to assessing LLM performance and bias.
arXiv:2603.09416v1 Announce Type: new Abstract: Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks, but they often propagate biases embedded in their training data, which is potentially impactful in sensitive domains like healthcare. While existing benchmarks evaluate biases related to individual social determinants of health (SDoH) such as gender or ethnicity, they often overlook interactions between these factors and lack context-specific assessments. This study investigates bias in LLMs by probing the relationships between gender and other SDoH in French patient records. Through a series of experiments, we found that embedded stereotypes can be probed using SDoH input and that LLMs rely on embedded stereotypes to make gendered decisions, suggesting that evaluating interactions among SDoH factors could usefully complement existing approaches to assessing LLM performance and bias.
Executive Summary
This article investigates the presence of gender stereotypes in Large Language Models (LLMs) through the lens of social determinants of health (SDoH). The study utilizes French patient records and experimental methods to demonstrate that LLMs rely on embedded stereotypes to make gendered decisions, highlighting the importance of evaluating interactions among SDoH factors. The findings suggest that existing approaches to assessing LLM performance and bias may be limited, and that a more comprehensive evaluation of SDoH interactions is warranted. The study's implications for healthcare and NLP are significant, as biased language models can perpetuate inequality in sensitive domains.
Key Points
- ▸ The study investigates gender stereotypes in LLMs using SDoH input and French patient records.
- ▸ LLMs rely on embedded stereotypes to make gendered decisions, suggesting a need for comprehensive bias evaluation.
- ▸ Evaluating interactions among SDoH factors could complement existing approaches to assessing LLM performance and bias.
Merits
Strength
The study's use of SDoH input and French patient records provides a nuanced understanding of bias in LLMs, highlighting the importance of context-specific assessments.
Strength
The study's experimental methods and findings contribute to a deeper understanding of how LLMs perpetuate inequality in sensitive domains like healthcare.
Demerits
Limitation
The study's findings are limited to a specific dataset (French patient records) and may not be generalizable to other contexts or languages.
Limitation
The study does not provide a comprehensive evaluation of the impact of biased language models on healthcare outcomes or access.
Expert Commentary
This study demonstrates the importance of evaluating bias in LLMs through the lens of SDoH interactions, particularly in sensitive domains like healthcare. The findings suggest that existing approaches to assessing LLM performance and bias may be limited, and that a more comprehensive evaluation of SDoH interactions is warranted. The study's use of French patient records and experimental methods provides a nuanced understanding of bias in LLMs, highlighting the importance of context-specific assessments. However, the study's limitations, including the specificity of the dataset and the lack of a comprehensive evaluation of impact on healthcare outcomes or access, should be addressed in future research.
Recommendations
- ✓ Future research should prioritize the evaluation of SDoH interactions and bias in LLMs, particularly in sensitive domains like healthcare.
- ✓ Developers and regulators should prioritize the development of LLMs that are fair, transparent, and accountable, with mechanisms for ongoing evaluation and improvement.