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arXiv · 2502.20898

A database to support the evaluation of gender biases in GPT-4o output

Luise Mehner·Lena Alicija Philine Fiedler·Sabine Ammon·Dorothea Kolossa
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Abstract

The widespread application of Large Language Models (LLMs) involves ethical risks for users and societies. A prominent ethical risk of LLMs is the generation of unfair language output that reinforces or exacerbates harm for members of disadvantaged social groups through gender biases (Weidinger et al., 2022; Bender et al., 2021; Kotek et al., 2023). Hence, the evaluation of the fairness of LLM outputs with respect to such biases is a topic of rising interest. To advance research in this field, promote discourse on suitable normative bases and evaluation methodologies, and enhance the reproducibility of related studies, we propose a novel approach to database construction. This approach enables the assessment of gender-related biases in LLM-generated language beyond merely evaluating their degree of neutralization.

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Luise Mehner, Lena Alicija Philine Fiedler, Sabine Ammon, Dorothea Kolossa. 2025-02-28. A database to support the evaluation of gender biases in GPT-4o output. https://arxiv.org/abs/2502.20898

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