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This page presents our preprocessing-based methods for bias mitigation and fairness enhancement in machine learning models. These methods operate directly on training data before model training to reduce bias at its source. Since machine learning models learn patterns from data, biased training data can lead to unfair or discriminatory predictions. By addressing bias beforehand, preprocessing methods help produce fairer and more responsible and unbiased outcomes / decisions.