dsldEDFFair: dsldEDFFair Wrappers

In dsld: Data Science Looks at Discrimination

dsldEDFFair Wrappers

Description

Explicitly Deweighted Features: control the effect of proxies related to sensitive variables for prediction.

Usage

dsldQeFairKNN(data, yName, sNames, deweightPars = NULL, 
  yesYVal = NULL, k = 25, scaleX = TRUE)
dsldQeFairRF(data, yName, sNames, deweightPars = NULL, nTree = 500, 
  minNodeSize = 10, mtry = floor(sqrt(ncol(data))), yesYVal = NULL)
dsldQeFairRidgeLin(data, yName, sNames, deweightPars = NULL)
dsldQeFairRidgeLog(data, yName, sNames, deweightPars = NULL, yesYVal)
## S3 method for class 'dsldQeFair'
predict(object,newx,...)

Arguments

data	Dataframe, training set.
yName	Name of the response variable column.
sNames	Name(s) of the sensitive attribute column(s).
deweightPars	Values for de-emphasizing variables in a split, e.g. 'list(age=0.2,gender=0.5)'. In the linear case, larger values means more deweighting, i.e. less influence of the given variable on predictions. For KNN and random forests, smaller values mean more deweighting.
scaleX	Scale the features. Defaults to TRUE.
yesYVal	Y value to be considered "yes," to be coded 1 rather than 0.
k	Number of nearest neighbors. In functions other than dsldQeFairKNN for which this is an argument, it is the number of neighbors to use in finding conditional probabilities via knnCalib.
nTree	Number of trees.
minNodeSize	Minimum number of data points in a tree node.
mtry	Number of variables randomly tried at each split.
object	An object returned by the dsld-EDFFAIR wrapper.
newx	New data to be predicted. Must be in the same format as original data.
...	Further arguments.

Details

The sensitive variables S are removed entirely, but there is concern that they still affect prediction indirectly, via a set C of proxy variables.

Linear EDF reduces the impact of the proxies through a shinkage process similar to that of ridge regression. Specifically, instead of minimizing the sum of squared errors SSE with respect to a coefficient vector b, we minimize SSE + the squared norm of Db, where D is a diagonal matrix with nonzero elements corresponding to C. Large values penalizing variables in C, thus shrinking them.

KNN EDF reduces the weights in Euclidean distance for variables in C. The random forests version reduces the probabilities that a proxy will be used in splitting a node.

By using various values of the deweighting parameters, the user can choose a desired position in the Fairness-Utility Tradeoff.

More details can be found in the references.

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

Value

The EDF functions return objects of class 'dsldQeFair', which include components for test and base accuracy, summaries of inputs and so on.

Author(s)

N. Matloff, A. Mittal, J. Tran

References

https://github.com/matloff/EDFfair

See Also

Matloff, Norman, and Wenxi Zhang. "A novel regularization approach to fair ML."
arXiv preprint arXiv:2208.06557 (2022).

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]

# dsldQeFairRidgeLin: deweight "occupation" and "age" columns
### also works for qeFairKNN and qeFairRF
lin <- dsldQeFairRidgeLin(train, "wageinc", "gender", deweightPars = 
                            list(occ=.4, age=.2))

# training results
lin$trainAcc
lin$trainCorrs

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

# also works with dsldQeFairRF, dsldQeFairKNN


# 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'))

# dsldQeFairKNN: deweight "decile score" column with "race" as the sensitive variable
# also works for qeFairRF, qeFairRidgeLog
knnOut <- dsldQeFairKNN(compas1, "two_year_recid", "race", 
                        list(decile_score=0.1), yesYVal = "Yes")

# training/testing results
knnOut$trainAcc 
knnOut$trainCorrs 
res = predict(knnOut, test) 
res$correlations
mean(test_y != round(res$preds$probs))

# also works with dsldQeFairRF, dsldQeFairRidgeLog
 

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