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)
dsldNclm(data, yName, sName, unfairness, covfun = cov, lambda = 0, 
   save.auxiliary = FALSE)
dsldZlm(data, yName, sName, unfairness)
dsldZlrm(data, yName, sName, unfairness)

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.

Details

See documentation for the fairml package.

Value

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

Author(s)

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

Examples

 

data(svcensus) 
data(compas1)

yName <- "wageinc"
sName <- "age"
frrmOut <- dsldFrrm(svcensus, yName, sName, 0.2, definition = "sp-komiyama") 
summary(frrmOut)
predict(frrmOut, svcensus[1:10,]) 

yName <- "two_year_recid"
sName <- "age"
fgrrmOut <- dsldFgrrm(compas1, yName, sName, 0.2, definition = "sp-komiyama")  
summary(fgrrmOut)
predict(fgrrmOut, compas1[c(1:10),]) 

dsld documentation built on Sept. 14, 2024, 1:08 a.m.