You can reduce bias in ML models by strategically modifying training data, but there's a trade-off: stricter fairness requirements cost more in data changes, and ensuring sufficient representation of intersectional groups is crucial for both fairness and model performance.
This paper addresses how to reduce bias in machine learning models, especially for underrepresented groups defined by multiple characteristics (like race and gender together). The authors propose a method that modifies training data to reduce bias while ensuring enough examples exist for all groups, and they measure the cost of achieving different levels of fairness.