LangFair
FreeLangFair is a Python library for conducting use-case-specific LLM bias and fairness assessments 
FreeFree tier
Inputs: textOutputs: text
About LangFair
LangFair is a comprehensive Python library for conducting bias and fairness assessments of large language model (LLM) use cases. It adopts a Bring Your Own Prompts (BYOP) approach, allowing users to tailor evaluations to specific use cases. The library focuses on output-based metrics that are practical for governance audits and real-world testing, without needing access to internal model states. It includes a documentation site with API reference, a framework for choosing bias and fairness metrics, demo notebooks, and supporting resources.
Key Features
Bring Your Own Prompts (BYOP) approach
Output-based metrics for governance audits
No need for internal model states
Integration with LangChain for generating LLM responses
Comprehensive API reference and demo notebooks
Framework for choosing bias and fairness metrics
Pros & Cons
Pros
- Addresses limitations of static benchmarks by focusing on use-case-specific prompts
- Practical for real-world governance audits and testing
- Open source and free to use
- Easy installation via pip
- Provides both bias and fairness metrics in one library
Cons
- Requires users to provide their own prompts and generate LLM responses
- Limited to output-based metrics; does not analyze internal model states
- Depends on LangChain for response generation, potentially adding complexity
Best For
Text generation bias assessmentSummarization bias assessmentClassification fairness assessmentRecommendation system fairness assessment
FAQ
What is LangFair?
LangFair is a comprehensive Python library designed for conducting bias and fairness assessments of large language model (LLM) use cases.
How do I install LangFair?
LangFair can be installed from PyPI using pip: pip install langfair.
Does LangFair require access to model internals?
No, LangFair focuses on output-based metrics and does not require access to internal model states.