Module 1
Understand where bias enters ML pipelines and how to measure and reduce it
Historical, representation, measurement, and aggregation bias explained
Equalized odds, demographic parity, and calibration — and why you can't have all three
Pre-processing, in-processing, and post-processing techniques with Fairlearn