aif360.algorithms.preprocessing.Reweighing

class aif360.algorithms.preprocessing.Reweighing(unprivileged_groups, privileged_groups)[source]

Reweighing is a preprocessing technique that Weights the examples in each (group, label) combination differently to ensure fairness before classification [4].

References

[4]F. Kamiran and T. Calders, “Data Preprocessing Techniques for Classification without Discrimination,” Knowledge and Information Systems, 2012.
Parameters:
  • unprivileged_groups (list(dict)) – Representation for unprivileged group.
  • privileged_groups (list(dict)) – Representation for privileged group.

Methods

fit Compute the weights for reweighing the dataset.
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 Return a new dataset with labels predicted by running this Transformer on the input.
transform Transform the dataset to a new dataset based on the estimated transformation.
__init__(unprivileged_groups, privileged_groups)[source]
Parameters:
  • unprivileged_groups (list(dict)) – Representation for unprivileged group.
  • privileged_groups (list(dict)) – Representation for privileged group.
fit(dataset)[source]

Compute the weights for reweighing the dataset.

Parameters:dataset (BinaryLabelDataset) – Dataset containing true labels.
Returns:Reweighing – Returns self.
transform(dataset)[source]

Transform the dataset to a new dataset based on the estimated transformation.

Parameters:dataset (BinaryLabelDataset) – Dataset that needs to be transformed.
Returns:dataset (BinaryLabelDataset) –
Dataset with transformed
instance_weights attribute.