Prejudice Remover Regularizer

The Prejudice Remover Regularizer is a technique used in machine learning to reduce biased or unfair outcomes in models, especially those that make decisions affecting people. It works by adding a special penalty during the model's training process, encouraging the model to minimize reliance on sensitive information such as race or gender. This helps ensure the model's predictions are fairer and less prejudiced, while still maintaining its overall accuracy. Essentially, it guides the model to make decisions based on relevant data without unfairly favoring or discriminating against certain groups.