Pre-processing

“Pre-processing” is about distorting the training sample to ensure that the model we obtain is “fair,” with respect to some criteria (defined in the previous chapters). The two standard techniques are either to modify the original dataset (and to distort features to make them “fair,” or independent of the sensitive attribute), or to use weights (as used in surveys to correct for biases). If there are poor theoretical guarantees, there are also legal issues with those techniques.