AI-AI Bias: large language models favor communications generated by large language models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916890723483648 |
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| author | Laurito, Walter Davis, Benjamin Grietzer, Peli Gavenčiak, Tomáš Böhm, Ada Kulveit, Jan |
| author_facet | Laurito, Walter Davis, Benjamin Grietzer, Peli Gavenčiak, Tomáš Böhm, Ada Kulveit, Jan |
| contents | Are large language models (LLMs) biased in favor of communications produced by LLMs, leading to possible antihuman discrimination? Using a classical experimental design inspired by employment discrimination studies, we tested widely used LLMs, including GPT-3.5, GPT-4 and a selection of recent open-weight models in binary choice scenarios. These involved LLM-based assistants selecting between goods (the goods we study include consumer products, academic papers, and film-viewings) described either by humans or LLMs. Our results show a consistent tendency for LLM-based AIs to prefer LLM-presented options. This suggests the possibility of future AI systems implicitly discriminating against humans as a class, giving AI agents and AI-assisted humans an unfair advantage. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_12856 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-AI Bias: large language models favor communications generated by large language models Laurito, Walter Davis, Benjamin Grietzer, Peli Gavenčiak, Tomáš Böhm, Ada Kulveit, Jan Computation and Language Artificial Intelligence Computers and Society Machine Learning Are large language models (LLMs) biased in favor of communications produced by LLMs, leading to possible antihuman discrimination? Using a classical experimental design inspired by employment discrimination studies, we tested widely used LLMs, including GPT-3.5, GPT-4 and a selection of recent open-weight models in binary choice scenarios. These involved LLM-based assistants selecting between goods (the goods we study include consumer products, academic papers, and film-viewings) described either by humans or LLMs. Our results show a consistent tendency for LLM-based AIs to prefer LLM-presented options. This suggests the possibility of future AI systems implicitly discriminating against humans as a class, giving AI agents and AI-assisted humans an unfair advantage. |
| title | AI-AI Bias: large language models favor communications generated by large language models |
| topic | Computation and Language Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2407.12856 |