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

TL;DR

This paper introduces a new database designed to evaluate gender biases in GPT-4o outputs, aiming to improve fairness assessments of large language models and support ethical AI development.

Contribution

It presents a novel approach for constructing a database that enables comprehensive evaluation of gender biases in LLM-generated language outputs.

Findings

Provides a new resource for bias evaluation in LLMs

Facilitates more nuanced assessments of gender bias beyond neutralization

Supports reproducibility and discourse in fairness research

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.