Pro-AI Bias in Large Language Models

• URL: http://arxiv.org/abs/2601.13749v1
• Date: Tue, 20 Jan 2026 09:03:57 GMT
• Title: Pro-AI Bias in Large Language Models
• Authors: Benaya Trabelsi, Jonathan Shaki, Sarit Kraus,
• Abstract summary: Large language models (LLMs) are increasingly employed for decision-support across multiple domains.<n>We investigate whether these models display a systematic preferential bias in favor of artificial intelligence (AI) itself.
• Score: 17.86909605285373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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