Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
About
We introduce a comprehensive framework for assessing gender fairness in large language models (LLMs), particularly in their treatment of both binary and non-binary genders. Existing research has largely focused on binary gender distinctions, neglecting the inclusivity of non-binary identities. To address this, the authors propose a novel metric that evaluates LLMs across seven dimensions. The study conducts extensive evaluations on 15 popular LLMs, revealing significant discrepancies in their ability to fairly represent diverse gender identities.
Details
- Date:
- Friday, April 4, 2025
- Time:
- 12:00 PM - 1:00 PM
- Location:
- CDS 1646
- Theme:
- Social Science
Speaker

Zhengyang Shan
Zhengyang is a third-year PhD student at Boston University's Faculty of Computing and Data Sciences. Her research interests lie in the evaluation, interpretability, and fairness of Large Language Models (LLMs).