Large language models (LLMs) are increasingly employed for decision-support across multiple domains. We investigate whether these models display a systematic preferential bias in favor of artificial intelligence (AI) itself. Across three complementary experiments, we find consistent evidence of pro-AI bias. First, we show that LLMs disproportionately recommend AI-related options in response to diverse advice-seeking queries, with proprietary models doing so almost deterministically. Second, we demonstrate that models systematically overestimate salaries for AI-related jobs relative to closely matched non-AI jobs, with proprietary models overestimating AI salaries more by 10 percentage points. Finally, probing internal representations of open-weight models reveals that ``Artificial Intelligence'' exhibits the highest similarity to generic prompts for academic fields under positive, negative, and neutral framings alike, indicating valence-invariant representational centrality. These patterns suggest that LLM-generated advice and valuation can systematically skew choices and perceptions in high-stakes decisions.

翻译：大型语言模型（LLMs）正日益广泛地应用于多领域的决策支持。本研究探讨这些模型是否对人工智能（AI）本身表现出系统性的偏好偏见。通过三项互补实验，我们发现了亲AI偏见的一致证据。首先，我们证明LLMs在面对多样化的寻求建议查询时，会不成比例地推荐与AI相关的选项，其中专有模型几乎以确定性方式呈现此倾向。其次，我们证明模型会系统性地高估AI相关职位相较于严格匹配的非AI职位的薪资，专有模型对AI薪资的高估幅度高出10个百分点。最后，通过对开源权重模型内部表征的探测发现，“人工智能”在积极、消极及中性框架下均表现出与学术领域通用提示词的最高相似度，表明其具有效价不变的表征中心性。这些模式表明，LLM生成的建议与价值评估可能在高风险决策中系统性地扭曲选择与认知。