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

2025-01-06

AI Summary

Large language models (LLMs) exhibit systemic biases along sensitive attributes such as gender, race, and age, yet existing fairness evaluation methods lack application-specific adaptability and reproducibility. Method: This paper introduces LangFair, the first use-case-oriented fairness evaluation framework for LLMs, accompanied by an open-source Python toolkit. It enables users to generate customized evaluation datasets via prompt-based data synthesis tailored to specific deployment scenarios; integrates multi-granularity fairness metrics—including equal opportunity difference and statistical parity—and provides an interpretable, decision-guided metric selection protocol; and implements an end-to-end automated evaluation pipeline. Contribution/Results: Empirical evaluations across multiple real-world LLM applications demonstrate that LangFair significantly improves bias detection efficiency and scenario-specific adaptability. By offering a reproducible, extensible, and open-source solution, LangFair advances practical, context-aware fairness assessment for LLMs.

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

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• Bias
• Fairness
• Language Models

Innovation

• Bias Evaluation
• Fairness Testing
• Language Models