fairmodels (version 1.2.1)

roc_pivot: Reject Option based Classification pivot

Description

Reject Option based Classifier is post-processing bias mitigation method. Method changes labels of favorable, privileged and close to cutoff observations to unfavorable and the opposite for unprivileged observations (changing unfavorable and close to cutoff observations to favorable, more in details). By this potentially wrongfully labeled observations are assigned different labels. Note that in y in DALEX explainer 1 should indicate favorable outcome.

Usage

roc_pivot(explainer, protected, privileged, cutoff = 0.5, theta = 0.1)

Value

DALEX explainer with changed y_hat. This explainer should be used ONLY by fairmodels as it contains unchanged predict function (changed predictions (y_hat) can possibly be invisible by DALEX functions and methods).

Arguments

explainer

created with explain

protected

factor, protected variables with subgroups as levels (sensitive attributes)

privileged

factor/character, level in protected denoting privileged subgroup

cutoff

numeric, threshold for all subgroups

theta

numeric, variable specifies maximal euclidean distance to cutoff resulting ing label switch

Details

Method implemented implemented based on article (Kamiran, Karim, Zhang 2012). In original implementation labels should be switched. Due to specific DALEX methods probabilities (y_hat) are assigned value in equal distance but other side of cutoff. The method changes explainers y_hat values in two cases.
1. When unprivileged subgroup is within (cutoff - theta, cutoff)
2. When privileged subgroup is within (cutoff, cutoff + theta)

References

Kamiran, Karim, Zhang 2012 https://ieeexplore.ieee.org/document/6413831/ ROC method

Examples

data("german")
data <- german
data$Age <- as.factor(ifelse(data$Age <= 25, "young", "old"))
y_numeric <- as.numeric(data$Risk) - 1

lr_model <- stats::glm(Risk ~ ., data = data, family = binomial())
lr_explainer <- DALEX::explain(lr_model, data = data[, -1], y = y_numeric)

fobject <- fairness_check(lr_explainer,
  protected = data$Age,
  privileged = "old"
)
plot(fobject)

lr_explainer_fixed <- roc_pivot(lr_explainer,
  protected = data$Age,
  privileged = "old"
)

fobject2 <- fairness_check(lr_explainer_fixed, fobject,
  protected = data$Age,
  privileged = "old",
  label = "lr_fixed"
)
fobject2
plot(fobject2)