AI Bias Assessment & Mitigation

About

This skill provides a comprehensive framework for identifying and addressing algorithmic bias in machine learning models and LLMs. It enables developers to perform systematic fairness evaluations across protected categories, detect hidden proxy variables, and implement mitigation strategies throughout the AI lifecycle—from pre-processing data to post-processing model outputs. It is essential for teams aiming to build ethical, compliant, and transparent AI systems that adhere to international fairness standards and legal frameworks like the Civil Rights Act or ADA.

Key Features

• LLM-specific bias testing via demographic prompt variations
• Fairness-aware model training and threshold optimization
• Automated proxy variable detection for protected attributes
• Pre-, in-, and post-processing mitigation technique implementation
• 5 GitHub stars
• Fairness metric calculation (Demographic Parity, Equalized Odds, etc.)

Use Cases

• Auditing a predictive model for disparate impact before production deployment
• Testing an LLM for consistent behavior across different demographic groups
• Implementing adversarial debiasing in a machine learning pipeline