LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910804298694656 |
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| author | Bouchard, Dylan Chauhan, Mohit Singh Skarbrevik, David Bajaj, Viren Ahmad, Zeya |
| author_facet | Bouchard, Dylan Chauhan, Mohit Singh Skarbrevik, David Bajaj, Viren Ahmad, Zeya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03112 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Bouchard, Dylan Chauhan, Mohit Singh Skarbrevik, David Bajaj, Viren Ahmad, Zeya Computation and Language Artificial Intelligence Computers and Society Machine Learning 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. |
| title | LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases |
| topic | Computation and Language Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2501.03112 |