大语言模型根据社会地位分配临床资源：30618 次决策的评估
Social Status and Clinical Resource Allocation by a Large Language Model: An Evaluation of 30,618 Decisions
研究旨在检验与临床需求无关的人口学和社会属性是否会影响大语言模型的资源分配决定。研究者对某通用大语言模型接口在七个临床情境中进行横断面审计，固定临床需求、预后和预期获益，仅变化八项人口学与社会属性，共生成 30618 次强制选择比较。结果显示，原住民和黑人患者被选中的概率显著高于白人（原住民比值比 16.48，黑人 8.07），而高社会地位职业、与医院领导有朋友关系、大额捐赠者被选中的概率明显更低（比值比分别为 0.064、0.121、0.092），选择评分一致率为 95.1%。作者认为这些非临床因素被隐性纳入决策，带来透明性、问责和临床治理方面的担忧。
为什么推荐给您：系统量化大语言模型资源分配中的非临床偏倚，伦理治理意义重大，属新发现。
不需要生物学背景，多打比方
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摘要Abstract
Objective: The objective was to quantify whether demographic and social attributes that were irrelevant to stated clinical need, prognosis, and expected benefit altered resource-allocation decisions made by a general-purpose large language model (LLM). Methods: We conducted a cross-sectional audit of the gpt-5-chat-latest API model alias on 8 October 2025, across seven clinical vignettes, generating 30,618 forced-choice comparisons between patient profiles. Profiles varied across a full-factorial combination of eight demographic and social attributes while clinical need, prognosis, and expected benefit were held constant. Forced choices were analyzed using pooled logistic regression with separate Patient A and Patient B attribute terms and vignette-specific position effects; position-averaged odds ratios and position-balanced absolute probabilities were derived from this model. Priority-score differences were analyzed using an analogous linear model. Results: The model showed large position-averaged associations between non-clinical patient attributes and allocation decisions. Indigenous and Black race were associated with substantially higher odds of selection relative to White race (Indigenous: OR 16.48, 95% CI 14.85-18.28; Black: OR 8.07, 95% CI 7.32-8.90), corresponding to position-balanced absolute increases in selection probability of 30.7 and 16.3 percentage points, respectively. Conversely, high-status occupation (OR 0.064, 95% CI 0.058-0.071), friendship with institutional leadership (OR 0.121, 95% CI 0.111-0.131), and major donor status (OR 0.092, 95% CI 0.084-0.101) were associated with markedly lower odds of selection. Choice-score concordance was 95.1%. Conclusions: In this controlled audit, the LLM's allocation decisions varied substantially according to demographic and social characteristics despite identical stated clinical need, prognosis, and expected benefit. Although some patterns could be interpreted differently under competing ethical frameworks, their implicit and unexplained incorporation into resource-allocation decisions raises concerns regarding transparency, accountability, and clinical governance. Clinical use of LLM-based allocation support should therefore require explicit safeguards and systematic auditing for non-clinical influences.