Robust Fairness-aware Learning Under Sample Selection Bias

Understand

The underlying assumption of many machine learning algorithms is that the training data and test data are drawn from the same distributions.

• However, the assumption is often violated in real world due to the sample selection bias between the training and test data.
• Previous research works focus on reweighing biased training data to match the test data and then building classification models on the reweighed training data.
• However, how to achieve fairness in the built classification models is under-explored.