LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases

TL;DR

LangFair is an open-source Python tool designed to help practitioners evaluate bias and fairness in large language models by generating datasets and calculating relevant metrics for specific use cases.

Contribution

It introduces a comprehensive package that simplifies bias assessment in LLMs, including dataset generation, metric calculation, and an actionable decision framework.

Findings

Facilitates bias detection in LLMs for various use cases

Provides an easy-to-use framework for bias evaluation

Supports informed decision-making on model fairness

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.