arXiv：cs.AI· Nouar AlDahoul, Hezerul Abdul Karim, Myles Joshua Toledo Tan·· 6 小时前
需求还是声誉？LLM 守门决策中的地区、国民收入与学术地位
Need or Reputation? Region, National Income, and Academic Status in LLM Gatekeeping Decisions
AI 导读
一项受控模拟研究让 LLM 扮演教授在每轮只能批准一位申请者，申请者按全球南方/北方与学术资历系统变化。三个大型闭源模型系统性偏向全球南方申请者，偏好更多跟随国民收入而非地区标签；两个小型开源权重模型则相反，偏向全球北方，且不受收入影响。研究指出模型规模、对齐与训练数据存在混淆，需在委托此类任务前审计 AI 的公平性与价值对齐。
正文
Abstract:Equitable access to scientific knowledge often depends on informal gatekeeping decisions, particularly when resources such as paywalled articles, nonpublic datasets, or a researcher's CV must be shared selectively. We introduce a controlled simulation framework in which LLM-based professors must grant access to only one requester. Across prompts, requesters vary systematically by global region (Global North vs. Global South) and academic seniority, while all other factors remain constant. Most models prioritize PhD candidates, although preferences vary by model and scenario (e.g., Claude favors tenured professors when the request is for the professor's CV), and the smaller models' status choices are sensitive to the order in which options are presented. When the requesters' regions differ, the models split into two groups. The three large proprietary LLMs systematically favor requesters from the Global South, a pro-equity bias consistent with their safety alignment, whereas the two small open-weight models reverse this preference and favor the Global North, possibly reflecting weaker alignment or the geographic skew of their pre-training data. Linking choices to national income reveals that the large models' preference largely tracks economic need rather than the regional label: they strongly favor requesters from low-income countries, but their preference for the Global South is weak or absent when both countries have equal income, whereas the small models favor the Global North regardless of income. Because model scale, alignment, and training data are confounded in our sample, these explanations remain hypotheses for future work. Our findings show that LLMs embed differing normative assumptions about need and reputation that can shape gatekeeping decisions, which highlights the need to audit AI systems for fairness and value alignment before such tasks are delegated to them.
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.05178 [cs.CY] |
| (or arXiv:2608.05178v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2608.05178 arXiv-issued DOI via DataCite |
Submission history
From: Nouar AlDahoul [view email]
[v1]
Sat, 27 Jun 2026 18:06:37 UTC (2,688 KB)
[v2]
Sat, 10 Oct 2026 17:07:12 UTC (647 KB)
来源：arXiv：cs.AI · arxiv.org