This project explores fairness-aware machine learning using various preprocessing techniques to mitigate bias in classification tasks. We implement and evaluate methods such as reweighing, FaUCI, and learned fair representations. The goal is to build models that maintain strong predictive performance while ensuring fair treatment across sensitive groups.
The doc is available at: https://fair-candidate-screening-system.readthedocs.io/en/latest/