Are generative AI text annotations systematically biased?

Fuente: arXiv
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Hauptverfasser: Stolwijk, Sjoerd B., Boukes, Mark, Trilling, Damian
Format: Preprint
Veröffentlicht: 2025
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author Stolwijk, Sjoerd B.
Boukes, Mark
Trilling, Damian
author_facet Stolwijk, Sjoerd B.
Boukes, Mark
Trilling, Damian
contents This paper investigates bias in GLLM annotations by conceptually replicating manual annotations of Boukes (2024). Using various GLLMs (Llama3.1:8b, Llama3.3:70b, GPT4o, Qwen2.5:72b) in combination with five different prompts for five concepts (political content, interactivity, rationality, incivility, and ideology). We find GLLMs perform adequate in terms of F1 scores, but differ from manual annotations in terms of prevalence, yield substantively different downstream results, and display systematic bias in that they overlap more with each other than with manual annotations. Differences in F1 scores fail to account for the degree of bias.
format Preprint
id arxiv_https___arxiv_org_abs_2512_08404
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Are generative AI text annotations systematically biased?
Stolwijk, Sjoerd B.
Boukes, Mark
Trilling, Damian
Computation and Language
Artificial Intelligence
This paper investigates bias in GLLM annotations by conceptually replicating manual annotations of Boukes (2024). Using various GLLMs (Llama3.1:8b, Llama3.3:70b, GPT4o, Qwen2.5:72b) in combination with five different prompts for five concepts (political content, interactivity, rationality, incivility, and ideology). We find GLLMs perform adequate in terms of F1 scores, but differ from manual annotations in terms of prevalence, yield substantively different downstream results, and display systematic bias in that they overlap more with each other than with manual annotations. Differences in F1 scores fail to account for the degree of bias.
title Are generative AI text annotations systematically biased?
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2512.08404