roc_pivot: Reject Option based Classification pivot

In fairmodels: Flexible Tool for Bias Detection, Visualization, and Mitigation

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)

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)

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).

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)

fairmodels documentation built on Aug. 24, 2022, 1:05 a.m.