fairness.profile.plot {fairml}

Profile Fair Models with Respect to Tuning Parameters

Description

Visually explore various aspect of a model over the range of possible values of the tuning parameters that control its fairness.

Usage

fairness.profile.plot(response, predictors, sensitive, unfairness,
  legend = FALSE, type = "coefficients", model, model.args = list(), cluster)

Arguments

response

a numeric vector, the response variable.

predictors

a numeric matrix or a data frame containing numeric and factor columns; the predictors.

sensitive

a numeric matrix or a data frame containing numeric and factor columns; the sensitive attributes.

unfairness

a vector of positive numbers in [0, 1], how unfair is the model allowed to be. The default value is seq(from = 0.00, to = 1, by = 0.02).

legend

a logical value, whether to add a legend to the plot.

type

a character string, either "coefficients" (the default), "constraints", "precision-recall" or "rmse".

model

a character string, the label of the model. Currently "nclm", "frrm", "fgrrm", "zlm" and "zlrm" are available.

model.args

additional arguments passed to model estimation.

cluster

an optional cluster object from package parallel, to fit models in parallel.

Details

fairness.profile.plot() fits the model for all the values of the argument unfairness, and produces a profile plot of the regression coefficients or the proportion of explained variance.

If type = "coefficients", the coefficients of the model are plotted against the values of unfairness.

If type = "constraints", the following quantities are plotted against the values of unfairness:

1. For model "nclm", and model "frrm" with definition = "sp-komiyama":

   1. the proportion of variance explained by the sensitive attributes (with respect to the response);

   2. the proportion of variance explained by the predictors (with respect to the response);

   3. the proportion of variance explained by the sensitive attributes (with respect to the combined sensitive attributes and predictors).

2. For model "frrm" with definition = "eo-komiyama":

   1. the proportion of variance explained by the sensitive attributes (with respect to the fitted values);

   2. the proportion of variance explained by the response (with respect to the fitted values);

   3. the proportion of variance explained by the sensitive attributes (with respect to the combined sensitive attributes and response).

3. For model "frrm" with definition = "if-berk", the ratio between the individual fairness loss computed for a given values of the constraint and that of the unrestricted model with unfairness = 1.

4. For model "fgrrm": same as for "frrm" for each definition.

5. For models "zlm" and "zlrm": the correlations between the fitted values (from fitted() with type = "link") and the sensitive attributes.

If type = "precision-recall" and the model is a classifier, the precision, recall and F1 measures are plotted against the values of unfairness.

If type = "rmse" and the model is a linear regression, the residuals mean square error are plotted against the values of unfairness.

Value

A trellis object containing a lattice plot.

Author(s)

Marco Scutari

Examples

data(vu.test)
fairness.profile.plot(response = vu.test$gaussian, predictors = vu.test$X,
  sensitive = vu.test$S, type = "coefficients", model = "nclm", legend = TRUE)
fairness.profile.plot(response = vu.test$gaussian, predictors = vu.test$X,
  sensitive = vu.test$S, type = "constraints", model = "nclm", legend = TRUE)
fairness.profile.plot(response = vu.test$gaussian, predictors = vu.test$X,
  sensitive = vu.test$S, type = "rmse", model = "nclm", legend = TRUE)

# profile plots fitting models in parallel.
## Not run: 
library(parallel)
cl = makeCluster(2)
fairness.profile.plot(response = vu.test$gaussian, predictors = vu.test$X,
  sensitive = vu.test$S, model = "nclm", cluster = cl)
stopCluster(cl)

## End(Not run)