Fairness and bias correction in Machine Learning

Version in Spanish: Equidad y corrección de sesgos en Aprendizaje Automático

Work in progress.

Metrics

Most statistical measures of fairness rely on different metrics, so we will start by defining them. When working with a binary classifier, both the predicted and the actual classes can take two values: positive and negative. Now let us start explaining the different possible relations between predicted and actual outcome:

• True positive (TP): The case where both the predicted and the actual outcome are in the positive class.
• True negative (TN): The case where both the predicted and the actual outcome are in the negative class.
• False positive (FP): A case predicted to be in the positive class when the actual outcome is in the negative one.
• False negative (FN): A case predicted to be in the negative class when the actual outcome is in the positive one.