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

• URL: http://arxiv.org/abs/2501.03112v1
• Date: Mon, 06 Jan 2025 16:20:44 GMT
• Title: LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases
• Authors: Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik, Viren Bajaj, Zeya Ahmad,
• Abstract summary: LangFair 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. To guide in metric selection, LangFair offers an actionable decision framework.
• Score: 0.0
• License:
• 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.

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