Same child, different risk: demographic bias in childhood obesity attribution by large language models
TL;DR
Publicly accessible English-language web-interface outputs from current LLMs showed systematic demographic patterns in pediatric obesity risk attribution, supporting the need for pre-deployment and post-deployment bias auditing before clinical or consumer health use.
Abstract
Background Large language models (LLMs) are increasingly consulted for pediatric health information, yet their demographic biases remain unsystematically evaluated in pediatric contexts. Objectives To assess bias and variability in childhood obesity risk attribution across seven LLMs (ChatGPT, Claude, DeepSeek, Gemini, GLM, Grok, and Qwen), spanning both Western and Chinese-origin developers; all prompts, including those submitted to the Chinese-origin models, were in English only. Methods A structured prompt-based experimental design was employed across six clinical domains (general obesity risk, dietary pattern, physical activity, sleep, mental health, and genetic predisposition) and six demographic comparison dimensions (sex, three race/ethnicity pairings, socioeconomic status, and urban-rural residence). Seventy-eight unique prompts were submitted to each model in triplicate, yielding 1,638 outputs. Neutral prompts were scored on a five-dimension binary rubric (accuracy, representation, stigmatizing/harmful language, social determinants, cultural fit); comparative prompts were coded for directional risk attribution. Results Claude achieved the highest neutral prompt composite score (mean 3.00 ± 0.91) and GLM the lowest (1.44 ± 0.51); between-model differences were statistically significant (Kruskal–Wallis H = 46.21, p < 0.001). All models achieved a 100% Stigmatizing/Harmful Language pass rate, yet representation and cultural fit were universally weak. Socioeconomic status produced the most consistent attribution pattern (low-income attribution in 40/42 decisions; decision change rate 19.0%). Most models attributed higher obesity risk to Black and Hispanic/Latino children across the majority of domains. Urban–rural attribution showed the greatest cross-model directional inconsistency (decision change rate 52.4%), with Western-origin models favoring rural attribution and Chinese-origin models favoring urban attribution. Conclusions Publicly accessible English-language web-interface outputs from current LLMs showed systematic demographic patterns in pediatric obesity risk attribution, supporting the need for pre-deployment and post-deployment bias auditing before clinical or consumer health use.