LLMs show minimal racial/sex bias in pain management audits

A new audit of large language models (LLMs) has found that while some models exhibit biases in pain management scenarios, the differences are often negligible or statistically insignificant after correction. The study examined models like GPT-4.1 mini, Claude Haiku 4.5, Gemini 2.5 Flash, Llama, DeepSeek, and Qwen, testing their responses to vignettes involving patients of different races and sexes. While some minor differences were detected, particularly in opioid prescription decisions, the study concluded that model variability and configuration settings influenced outcomes more than patient identity. The researchers recommend robust fairness audits that account for model instability and diverse patient representation. AI

IMPACT Suggests current LLMs may be less prone to perpetuating racial and sex biases in sensitive applications like pain management than previously feared.

RANK_REASON Academic paper detailing a bias audit of multiple LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Anthropic
• Claude Haiku-4-5
• DeepSeek
• Gemini 2.5-Flash
• generative pre-trained transformer
• GPT-4.1 mini
• gpt-oss
• llama
• Meta*
• OpenAI
• Qwen

• paper
• safety

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