  Are large language models (LLMs) biased towards text generated by LLMs over
text authored by humans, leading to possible anti-human bias? Utilizing a
classical experimental design inspired by employment discrimination studies, we
tested widely-used LLMs, including GPT-3.5 and GPT4, in binary-choice
scenarios. These involved LLM-based agents selecting between products and
academic papers described either by humans or LLMs under identical conditions.
Our results show a consistent tendency for LLM-based AIs to prefer
LLM-generated content. This suggests the possibility of AI systems implicitly
discriminating against humans, giving AI agents an unfair advantage.
