This document introduces on how to use
create audit reports with different tasks throughout the fairness
There are three main sections for this document. Which describe the details for the task, the model and the interpretability of the parameters.
Jump to section:
In this fairness report, we investigate the fairness of the following task:
#> <TaskClassif:adult_train> (700 x 6) #> * Target: target #> * Properties: twoclass #> * Features (5): #> - fct (4): education, marital_status, race, sex #> - int (1): age #> * Protected attribute: pta
Here we the basic details for the task.
|Number of observations:||700|
|Number of features:||6|
|Feature Names:||age, education, marital_status, race, sex|
We also report the number of missing values, types and the levels for each feature:
|5||education||factor||10th , 11th , 12th , 1st-4th , 5th-6th , 7th-8th , 9th , Assoc-acdm , Assoc-voc , Bachelors , Doctorate , HS-grad , Masters , Preschool , Prof-school , Some-college||NA||FALSE||0|
|8||marital_status||factor||Divorced , Married-AF-spouse , Married-civ-spouse , Married-spouse-absent, Never-married , Separated , Widowed||NA||FALSE||0|
|10||race||factor||Amer-Indian-Eskimo, Asian-Pac-Islander, Black , Other , White||NA||FALSE||0|
We first look at the label distribution:
We could see the model that has been used in
#> <LearnerClassifRpart:classif.rpart>: Classification Tree #> * Model: - #> * Parameters: xval=0 #> * Packages: mlr3, rpart #> * Predict Types: response, [prob] #> * Feature Types: logical, integer, numeric, factor, ordered #> * Properties: importance, missings, multiclass, selected_features, #> twoclass, weights
We furthermore report more than one fairness metric. Below metrics are the mean across all the resample results.
We can furthermore employ several visualizations to report the fairness. For example, the fairness and accuracy trade off, compare metrics visualization and the fairness prediction density of our model. For more detailed usage and examples, you may want to check the visualization vignette.
Finally, we use the external package to gain further insight into our
model. For the following example we use the
iml package as
a demonstration. We need first extract the learner from
resample_result and wrap it in a
You could generate the variable importance plot like this
Or generate the feature effects plot:
For more details on interpretability, check the documentation of the