aif360.algorithms.inprocessing.PrejudiceRemover

class aif360.algorithms.inprocessing.PrejudiceRemover(eta=1.0, sensitive_attr='', class_attr='')[source]

Prejudice remover is an in-processing technique that adds a discrimination-aware regularization term to the learning objective [6].

References

Parameters:
  • eta (double, optional) – fairness penalty parameter

  • sensitive_attr (str, optional) – name of protected attribute

  • class_attr (str, optional) – label name

Methods

fit

Learns the regularized logistic regression model.

fit_predict

Train a model on the input and predict the labels.

fit_transform

Train a model on the input and transform the dataset accordingly.

predict

Obtain the predictions for the provided dataset using the learned prejudice remover model.

transform

Return a new dataset generated by running this Transformer on the input.

__init__(eta=1.0, sensitive_attr='', class_attr='')[source]
Parameters:
  • eta (double, optional) – fairness penalty parameter

  • sensitive_attr (str, optional) – name of protected attribute

  • class_attr (str, optional) – label name

fit(dataset)[source]

Learns the regularized logistic regression model.

Parameters:

dataset (BinaryLabelDataset) – Dataset containing true labels.

Returns:

PrejudiceRemover – Returns self.

predict(dataset)[source]

Obtain the predictions for the provided dataset using the learned prejudice remover model.

Parameters:

dataset (BinaryLabelDataset) – Dataset containing labels that needs to be transformed.

Returns:

dataset (BinaryLabelDataset) – Transformed dataset.