Fighting Bias in AI Language Models
A toolkit to detect and mitigate prediction biases in LLMs
FairPy is a comprehensive toolkit for evaluating and addressing bias in large language models by helping identify and reduce unfair token predictions.
- Quantifies mathematical frameworks that measure bias in models like BERT and GPT-2
- Provides tools to detect biases inherited from training data distributions
- Implements mitigation techniques to reduce biased predictions
- Focuses on practical applications for improving LLM fairness
Security Impact: By identifying and correcting biases in AI language systems, FairPy helps prevent harmful outputs, reduces discrimination risks, and addresses critical security vulnerabilities in deployed AI systems.
FairPy: A Toolkit for Evaluation of Prediction Biases and their Mitigation in Large Language Models