PlotFairness: Plot the principal fairness

In aihuman: Experimental Evaluation of Algorithm-Assisted Human Decision-Making

Plot the principal fairness

Description

See Figure 5 for example.

Usage

PlotFairness(
  res,
  top.margin = 0.01,
  y.max = 0.2,
  y.min = -0.1,
  r.labels = c("Safe", "Easily\nPreventable", "Preventable", "Risky"),
  label = TRUE
)

Arguments

res	The data frame generated from CalFairness.
top.margin	The index of subgroups (within the output of CalAPCE/CalAPCEparallel) that corresponds to the protected attributes.
y.max	Maximum value of y-axis.
y.min	Minimum value of y-axis.
r.labels	Label of each principal stratum.
label	A logical argument whether to specify label.

Value

A data.frame of the delta.

Examples

data(synth)
subgroup_synth <- list(
  1:nrow(synth), which(synth$Sex == 0), which(synth$Sex == 1),
  which(synth$Sex == 1 & synth$White == 0), which(synth$Sex == 1 & synth$White == 1)
)
sample_mcmc <- AiEvalmcmc(data = synth, n.mcmc = 10)
sample_apce <- CalAPCE(
  data = synth, mcmc.re = sample_mcmc, subgroup = subgroup_synth,
  burnin = 0
)
sample_fair <- CalFairness(sample_apce)
PlotFairness(sample_fair, y.max = 0.4, y.min = -0.4, r.labels = c(
  "Safe", "Preventable 1",
  "Preventable 2", "Preventable 3", "Risky"
))

aihuman documentation built on April 12, 2025, 1:47 a.m.