LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases
Year: 2025
Type: software-paper
Abstract: Large Language Models (LLMs) have been observed to exhibit bias in numerous ways, potentially creating or worsening outcomes for specific groups identified by protected attributes such as sex, race, sexual orientation, or age. To help address this gap, we introduce LangFair, an open-source Python package that aims to equip LLM practitioners with the tools to evaluate bias and fairness risks relevant to their specific use cases. The package offers functionality to easily generate evaluation datasets, comprised of LLM responses to use-case-specific prompts, and subsequently calculate applicable metrics for the practitioner's use case. To guide in metric selection, LangFair offers an actionable decision framework.
Source: arXiv (Cornell University)
Authors: Bouchard, Dylan, Chauhan, Mohit Singh, Skarbrevik, David, Bajaj, Viren, Ahmad, Zeya
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W4406123638