Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study has shown that LLMs encode social biases and manifest in clinical tasks. |
| Approach: | They use mechanistic interpretability to uncover biases within LLMs . they find gender information is highly localized in MLP layers . |
| Outcome: | The proposed method can reveal biases and representations within LLMs in healthcare. |
Similar Papers
Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective (2025.coling-main)
Copied to clipboard
| 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. |
| Approach: | They evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLM to real-world healthcare tasks. |
| Outcome: | The proposed models perform poorly in real-world healthcare tasks and are inconsistent with existing learning frameworks. |
Can LLMs Replace Clinical Doctors? Exploring Bias in Disease Diagnosis by Large Language Models (2024.findings-emnlp)
Copied to clipboard
| 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 . |
| Approach: | They propose a prompt-based approach to alleviate the bias in disease prediction with LLMs. |
| Outcome: | The proposed model alleviates the observed bias in disease prediction with LLMs. |
How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making? (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that LLMs exhibit social biases inherited from training data. |
| Approach: | They propose a framework for evaluation and mitigation of bias in Large Language Models applied to complex clinical cases using a dataset based on the JAMA Clinical Challenge. |
| Outcome: | The proposed framework employs multiple choice questions and explanations to evaluate gender and ethnicity biases in LLMs. |
Addressing Healthcare-related Racial and LGBTQ+ Biases in Pretrained Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Pretrained language models (PLMs) propagate social stigmas and stereotypes, a critical concern given their widespread use. |
| Approach: | They adapt two intrinsic bias benchmarks to quantify racial and LGBTQ+ biases in prevalent PLMs and empirically evaluate the effectiveness of various debiasing methods in mitigating these biase. |
| Outcome: | The proposed methods reduce biases without compromising performance in downstream tasks. |
Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that large language models carry implicit biases across race, gender, and religion . prior studies documented such biase based on text generation and classification tasks . |
| Approach: | They investigate bias in large language models by controlling metadata on author metadata . authors found affiliation bias favoring authors from highly ranked institutions . |
| Outcome: | The proposed model favors authors from highly ranked institutions, the authors show . the model also favors author affiliations from highly-ranked institutions . |
Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing benchmarks evaluate biases related to individual social determinants of health (SDoH) but they overlook interactions between these factors and lack context-specific assessments. |
| Approach: | They investigated the relationship between gender and other SDoH in french patient records to determine whether LLMs rely on embedded stereotypes to make gendered decisions. |
| Outcome: | The proposed models can probe stereotypes and make gendered decisions based on the data. |
Veracity Bias and Beyond: Uncovering LLMs’ Hidden Beliefs in Problem-Solving Reasoning (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been aligned to avoid harmful biases and stereotypes, but recent studies have revealed the superficial nature of this alignment. |
| Approach: | They propose to use large language models to avoid harmful biases and stereotypes by assigning personas to LLMs to observe decision discrepancies in social scenarios or asking them to associate specific attributes with social targets. |
| Outcome: | The proposed models attribute fewer correct solutions and more incorrect ones to African-American groups in math and coding, while Asian authorships are least preferred in writing evaluation. |
Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | 211 studies on the demographic representativeness of large language models have conflicting results . 29% of the studies report positive conclusions on the representativeness, 30% do not evaluate LLMs across multiple demographic categories or within demographic subcategories. |
| Approach: | 211 papers review the representativeness of large language models . authors recommend more precise evaluation methods and comprehensive documentation of demographic attributes . |
| Outcome: | 211 studies on the representativeness of large language models are reviewed . 29% of the studies report positive conclusions, but 30% fail to specify subcategories . authors recommend more precise evaluation methods and documentation of demographic attributes . |
Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in language generation models can be used to assist users in a variety of tasks, but there are risks associated with introducing LLM biases into consequential decisions. |
| Approach: | They propose to use a template-generated dataset to measure subtler correlated decisions that LLMs make between social groups and unrelated positive and negative attributes. |
| Outcome: | The proposed model can be used to evaluate progress in more generalized biases and extend the benchmark with minimal human annotation. |
A Survey of LLM-based Agents in Medicine: How far are we from Baymax? (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are transforming healthcare through their ability to understand and assist with medical tasks. |
| Approach: | They analyze system profiles, clinical planning, medical reasoning frameworks, and external capacity enhancement. |
| Outcome: | The findings highlight the future directions in medical reasoning, physical system integration, and training simulations. |