aif360.algorithms.inprocessing.AdversarialDebiasing

class aif360.algorithms.inprocessing.AdversarialDebiasing(unprivileged_groups, privileged_groups, scope_name, sess, seed=None, adversary_loss_weight=0.1, num_epochs=50, batch_size=128, classifier_num_hidden_units=200, debias=True)[source]

Adversarial debiasing is an in-processing technique that learns a classifier to maximize prediction accuracy and simultaneously reduce an adversary’s ability to determine the protected attribute from the predictions [5]. This approach leads to a fair classifier as the predictions cannot carry any group discrimination information that the adversary can exploit.

References

[5]B. H. Zhang, B. Lemoine, and M. Mitchell, “Mitigating Unwanted Biases with Adversarial Learning,” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, 2018.
Parameters:
  • unprivileged_groups (tuple) – Representation for unprivileged groups
  • privileged_groups (tuple) – Representation for privileged groups
  • scope_name (str) – scope name for the tenforflow variables
  • sess (tf.Session) – tensorflow session
  • seed (int, optional) – Seed to make predict repeatable.
  • adversary_loss_weight (float, optional) – Hyperparameter that chooses the strength of the adversarial loss.
  • num_epochs (int, optional) – Number of training epochs.
  • batch_size (int, optional) – Batch size.
  • classifier_num_hidden_units (int, optional) – Number of hidden units in the classifier model.
  • debias (bool, optional) – Learn a classifier with or without debiasing.

Methods

fit Compute the model parameters of the fair classifier using gradient descent.
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 fair classifier learned.
transform Return a new dataset generated by running this Transformer on the input.
__init__(unprivileged_groups, privileged_groups, scope_name, sess, seed=None, adversary_loss_weight=0.1, num_epochs=50, batch_size=128, classifier_num_hidden_units=200, debias=True)[source]
Parameters:
  • unprivileged_groups (tuple) – Representation for unprivileged groups
  • privileged_groups (tuple) – Representation for privileged groups
  • scope_name (str) – scope name for the tenforflow variables
  • sess (tf.Session) – tensorflow session
  • seed (int, optional) – Seed to make predict repeatable.
  • adversary_loss_weight (float, optional) – Hyperparameter that chooses the strength of the adversarial loss.
  • num_epochs (int, optional) – Number of training epochs.
  • batch_size (int, optional) – Batch size.
  • classifier_num_hidden_units (int, optional) – Number of hidden units in the classifier model.
  • debias (bool, optional) – Learn a classifier with or without debiasing.
fit(dataset)[source]

Compute the model parameters of the fair classifier using gradient descent.

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

Obtain the predictions for the provided dataset using the fair classifier learned.

Parameters:dataset (BinaryLabelDataset) – Dataset containing labels that needs to be transformed.
Returns:dataset (BinaryLabelDataset) – Transformed dataset.