Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts

ACL ARR 2025 February Submission7884 Authors

Abstract: Large vision-language models (LVLMs) have recently achieved significant progress, demonstrating strong capabilities in open-world visual understanding. However, it is not yet clear how LVLMs address demographic biases in real life, especially the disparities across attributes such as gender, skin tone, age and race. In this paper, We empirically investigate visual fairness in several mainstream LVLMs by auditing their performance disparities across demographic attributes using public fairness benchmark datasets (e.g., FACET, UTKFace). Our fairness evaluation framework employs direct and single-choice question prompts on visual question-answering/classification tasks. Despite advancements in visual understanding, our zero-shot prompting results show that both open-source and closed-source LVLMs continue to exhibit fairness issues across different prompts and demographic groups. Furthermore, we propose a potential multi-modal Chain-of-thought (CoT) based strategy for bias mitigation, applicable to both open-source and closed-source LVLMs. This approach enhances transparency and offers a scalable solution for addressing fairness, providing a solid foundation for future bias reduction efforts.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: Large Vision-Language Models, Fairness

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 7884