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

2025-02-28

AI Summary

This work addresses the challenge of systematically evaluating implicit and structural gender bias in large language model (LLM) outputs—particularly in models like GPT-4o—where conventional fairness metrics lack sensitivity and interpretability. Methodologically, we propose a multidimensional, reproducible fairness assessment paradigm integrating human annotation, adversarial prompt engineering, and cross-contextual template generation. Our structured corpus is annotated along three core dimensions: occupation–gender associations, pronoun consistency, and sociocultural role stereotyping—enabling norm-sensitive, fine-grained bias analysis beyond superficial neutrality measures. As a key contribution, we release the first open-source, GPT-4o–specific gender bias evaluation benchmark, featuring transparent annotation protocols, standardized prompts, and interpretable bias scores. This resource significantly enhances result explainability, cross-study comparability, and methodological transparency, establishing a standardized, reproducible infrastructure for LLM fairness research.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Evaluate gender biases in GPT-4 outputs
• Develop database for ethical LLM assessment
• Enhance reproducibility in bias evaluation studies

Innovation

• Database construction for gender bias evaluation
• Assessing biases beyond neutralization degree
• Enhancing reproducibility in LLM fairness studies