This is an experimental approach. Please have it in mind when using it. Fairness_check_regression enables to check fairness in regression models. It uses so-called probabilistic classification to approximate fairness measures. The metrics in use are independence, separation, and sufficiency. The intuition behind this method is that the closer to 1 the metrics are the better. When all metrics are close to 1 then it means that from the perspective of a predictive model there are no meaningful differences between subgroups.

fairness_check_regression(
x,
...,
protected = NULL,
privileged = NULL,
label = NULL,
epsilon = NULL,
verbose = TRUE,
colorize = TRUE
)

## Arguments

x object created with explain or of class fairness_regression_object. It can be multiple fairness_objects, multiple explainers, or combination on both, as long as they predict the same data. If at least one fairness_object is provided there is no need to pass protected and privileged parameters. Explainers must be of type regression possibly more objects created with explain and/or objects of class fairness_regression_object factor, protected variable (also called sensitive attribute), containing privileged and unprivileged groups factor/character, one value of protected, denoting subgroup suspected of the most privilege character, vector of labels to be assigned for explainers, default is explainer label. numeric, boundary for fairness checking, lowest/maximal acceptable metric values for unprivileged. Default value is 0.8. logical, whether to print information about creation of fairness object logical, whether to print information in color

## Details

Sometimes during metric calculation faze approximation algorithms (logistic regression models) might not coverage properly. This might indicate that the membership to subgroups has strong predictive power.

## References

Steinberg, Daniel & Reid, Alistair & O'Callaghan, Simon. (2020). Fairness Measures for Regression via Probabilistic Classification. - https://arxiv.org/pdf/2001.06089.pdf

## Examples


set.seed(123)
data <- data.frame(
x = c(rnorm(500, 500, 100), rnorm(500, 400, 200)),
pop = c(rep("A", 500), rep("B", 500))
)

data$y <- rnorm(length(data$x), 1.5 * data$x, 100) # create model model <- lm(y ~ ., data = data) # create explainer exp <- DALEX::explain(model, data = data, y = data$y)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  1000  rows  3  cols
#>   -> target variable   :  1000  values
#>   -> predict function  :  yhat.lm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task regression (  default  )
#>   -> predicted values  :  numerical, min =  -269.546 , mean =  681.4906 , max =  1444.562
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -302.6659 , mean =  -9.167582e-14 , max =  332.7938
#>   A new explainer has been created!

# create fobject
fobject <- fairness_check_regression(exp, protected = data$pop, privileged = "A") #> Creating fairness regression object #> -> Privileged subgroup : character ( Ok ) #> -> Protected variable : factor ( changed from character ) #> -> Fairness objects : 0 objects #> -> Checking explainers : 1 in total ( compatible ) #> -> Metric calculation : 3/3 metrics calculated for all models #> Fairness regression object created succesfully #> # results fobject #> #> Fairness check regression for models: lm #> #> lm passes 3/3 metrics #> Total loss: 0.243362 #> plot(fobject) # \donttest{ model_ranger <- ranger::ranger(y ~ ., data = data, seed = 123) exp2 <- DALEX::explain(model_ranger, data = data, y = data$y)
#> Preparation of a new explainer is initiated
#>   -> model label       :  ranger  (  default  )
#>   -> data              :  1000  rows  3  cols
#>   -> target variable   :  1000  values
#>   -> predict function  :  yhat.ranger  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package ranger , ver. 0.13.1 , task regression (  default  )
#>   -> predicted values  :  numerical, min =  210.6774 , mean =  681.3779 , max =  987.8878
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -668.0995 , mean =  0.1126996 , max =  629.5469
#>   A new explainer has been created!

fobject <- fairness_check_regression(exp2, fobject)
#> Creating fairness regression object
#> -> Privileged subgroup		: character ( from first fairness object  )
#> -> Protected variable		: factor ( from first fairness object  )
#> -> Fairness objects		: 1 object (  compatible  )
#> -> Checking explainers		: 2 in total (  compatible  )
#> -> Metric calculation		: 3/3 metrics calculated for all models
#>  Fairness regression object created succesfully
#>

# results
fobject
#>
#> Fairness check regression for models: ranger, lm
#>
#> ranger passes 0/3 metrics
#> Total loss:  2.086092
#>
#> lm passes 3/3 metrics
#> Total loss:  0.243362
#>

plot(fobject)

# }