aif360.algorithms.preprocessing.DisparateImpactRemover

class aif360.algorithms.preprocessing.DisparateImpactRemover(repair_level=1.0, sensitive_attribute='')

Disparate impact remover is a preprocessing technique that edits feature values increase group fairness while preserving rank-ordering within groups [1].

References

[1]

M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, “Certifying and removing disparate impact.” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015.

Parameters:

• repair_level (float) – Repair amount. 0.0 is no repair while 1.0 is full repair.

• sensitive_attribute (str) – Single protected attribute with which to do repair.

Methods

fit	Train a model on the input.
fit_predict	Train a model on the input and predict the labels.
fit_transform	Run a repairer on the non-protected features and return the transformed dataset.
predict	Return a new dataset with labels predicted by running this Transformer on the input.
transform	Return a new dataset generated by running this Transformer on the input.

__init__(repair_level=1.0, sensitive_attribute='')

Parameters:

• repair_level (float) – Repair amount. 0.0 is no repair while 1.0 is full repair.

• sensitive_attribute (str) – Single protected attribute with which to do repair.

fit_transform(dataset)

Run a repairer on the non-protected features and return the transformed dataset.

Parameters:

dataset (BinaryLabelDataset) – Dataset that needs repair.

Returns:

dataset (BinaryLabelDataset) – Transformed Dataset.

Note

In order to transform test data in the same manner as training data, the distributions of attributes conditioned on the protected attribute must be the same.