dsldFairML: dsldFairML Wrappers

In dsld: Data Science Looks at Discrimination

dsldFairML Wrappers

Description

Fair machine learning models: estimation and prediction. The following functions provide wrappers for some functions in the fairML package.

Usage

dsldFrrm(data, yName, sName, unfairness, definition = "sp-komiyama", 
   lambda = 0, save.auxiliary = FALSE)
dsldFgrrm(data, yName, sName, unfairness, definition = "sp-komiyama", 
   family = "binomial", lambda = 0, save.auxiliary = FALSE, yesYVal)
dsldNclm(data, yName, sName, unfairness, covfun = cov, lambda = 0, 
   save.auxiliary = FALSE)
dsldZlm(data, yName, sName, unfairness)
dsldZlrm(data, yName, sName, unfairness, yesYVal)

Arguments

data	Data frame.
yName	Name of the response variable column.
sName	Name(s) of the sensitive attribute column(s).
unfairness	A number in (0, 1]. Degree of unfairness allowed in the model. A value (very near) 0 means the model is completely fair, while a value of 1 means the model is not constrained to be fair at all.
covfun	A function computing covariance matrices.
definition	Character string, the label of the definition of fairness. Currently either 'sp-komiyama', 'eo-komiyama' or 'if-berk'.
family	A character string, either 'gaussian' to fit linear regression, 'binomial' for logistic regression, 'poisson' for log-linear regression, 'cox' for Cox proportional hazards regression, or 'multinomial' for multinomial logistic regression.
lambda	Non-negative number, a ridge-regression penalty coefficient.
save.auxiliary	A logical value, whether to save the fitted values and the residuals of the auxiliary model that constructs the debiased predictors.
yesYVal	Y value to be considered 'yes', to be coded 1 rather than 0.

Details

See documentation for the fairml package.

The DSLD package extends functionality by providing both accuracy (MAPE or misclassification rate) and fairness (correlation) on the training set when fitting the model.

Value

An object of class 'dsldFairML', which includes the model information, yName, sName, and model training details.

Author(s)

A. Mittal, S. Martha, B. Ouattara, B. Zarate, J. Tran

Examples

 

# regression example
data(svcensus)

# test/train splits
n <- nrow(svcensus)
train_idx <- sample(seq_len(n), size = 0.7 * n) 
train <- svcensus[train_idx, ]
test  <- svcensus[-train_idx, -4]
test_y <- svcensus[-train_idx, 4]

# train frrm model // also works with nclm and zlm
frrmOut <- dsldFrrm(data = train, yName = 'wageinc', sName = 'gender', 
                    unfairness = 0.2, definition = "sp-komiyama") 

# training results
summary(frrmOut)
frrmOut$trainCorrs
frrmOut$trainAcc

# testing results
res <- predict(frrmOut, test) 
res$correlations
mean(abs(res$preds - test_y))

# also works with dsldNclm, dsldZlm

# classification example
data(compas1)

# test/train splits
n <- nrow(compas1)
train_idx <- sample(seq_len(n), size = 0.7 * n) 
train <- compas1[train_idx, ]
test  <- compas1[-train_idx, -8]
test_y <- compas1[-train_idx, 8]
test_y <- as.factor(as.integer(test_y== 'Yes'))

# train fgrrm model // also works with zlrm
fgrrmOut <- dsldFgrrm(train, yName = "two_year_recid", 
                      sName = "age", unfairness = 0.05, 
                      definition = "sp-komiyama", 
                      yesYVal = 'Yes')  
# training results
summary(fgrrmOut)
fgrrmOut$trainCorrs
fgrrmOut$trainAcc

# testing results
res <- predict(fgrrmOut, test) 
res$correlations
mean(test_y != round(res$preds))

# also works with dsldZlm

dsld documentation built on Sept. 14, 2025, 1:07 a.m.