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
- 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
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.
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.