Machine learning models in HR (attrition, promotion, performance, pay equity) can unintentionally learn proxy variables for protected characteristics (e.g., gender, race, age), even when those attributes are excluded.

This notebook demonstrates a practical, auditable approach to:

Identify features that strongly correlate with protected classes Quantify proxy risk using correlation thresholds Support bias mitigation decisions before model training