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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/inprocessing_adversarial_debiasing.R
\name{adversarial_debiasing}
\alias{adversarial_debiasing}
\title{Adversarial Debiasing}
\usage{
adversarial_debiasing(
unprivileged_groups,
privileged_groups,
scope_name = "current",
sess = tf$compat$v1$Session(),
seed = NULL,
adversary_loss_weight = 0.1,
num_epochs = 50L,
batch_size = 128L,
classifier_num_hidden_units = 200L,
debias = TRUE
)
}
\arguments{
\item{unprivileged_groups}{A list with two values: the column of the protected class and the value indicating representation for unprivileged group.}
\item{privileged_groups}{A list with two values: the column of the protected class and the value indicating representation for privileged group.}
\item{scope_name}{Scope name for the tensorflow variables.}
\item{sess}{tensorflow session}
\item{seed}{Seed to make \code{predict} repeatable. If not, \code{NULL}, must be an integer.}
\item{adversary_loss_weight}{Hyperparameter that chooses the strength of the adversarial loss.}
\item{num_epochs}{Number of training epochs. Must be an integer.}
\item{batch_size}{Batch size. Must be an integer.}
\item{classifier_num_hidden_units}{Number of hidden units in the classifier model. Must be an integer.}
\item{debias}{Learn a classifier with or without debiasing.}
}
\description{
Adversarial debiasing is an in-processing technique that learns a classifier to maximize prediction accuracy
and simultaneously reduce an adversary's ability to determine the protected attribute from the predictions
}
\examples{
\dontrun{
load_aif360_lib()
ad <- adult_dataset()
p <- list("race", 1)
u <- list("race", 0)
sess <- tf$compat$v1$Session()
plain_model <- adversarial_debiasing(privileged_groups = p,
unprivileged_groups = u,
scope_name = "debiased_classifier",
debias = TRUE,
sess = sess)
plain_model$fit(ad)
ad_nodebiasing <- plain_model$predict(ad)
}
}
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