# Debiasing Methods

# Introduction: Fairness Pipeline Operators

Given we detected some form of bias during bias auditing, we are often interested in obtaining fair(er) models. There are several ways to achieve this, such as collecting additional data or finding and fixing errors in the data. Assuming there are no biases in the data and labels, one other option is to debias models using either preprocessing, postprocessing and inprocessing methods. `mlr3fairness` provides some operators as `PipeOp`s for `mlr3pipelines`. If you are not familiar with `mlr3pipelines`, the mlr3 book contains an introduction.

We again showcase debiasing using the `adult_train` task:

    library(mlr3)
    library(mlr3fairness)
    library(mlr3pipelines)

    task = tsk("adult_train")

# Reweighing algorithms

`mlr3fairness` implements 2 reweighing-based algorithms: `reweighing_wts` and `reweighing_os`. `reweighing_wts` adds observation weights to a `Task` that can counteract imbalances between the conditional probabilities \(P(Y | pta)\).

key | output.num | input.type.train | input.type.predict | output.type.train
--- | --- | --- | --- | ---
EOd | 1 | TaskClassif | TaskClassif | NULL
reweighing_os | 1 | TaskClassif | TaskClassif | TaskClassif
reweighing_wts | 1 | TaskClassif | TaskClassif | TaskClassif

We fist instantiate the `PipeOp`:

    p1 = po("reweighing_wts")

and directly add the weights:

    t1 = p1$train(list(task))[[1]]

Often we directly combine the `PipeOp` with a `Learner` to automate the preprocessing (see `learner_rw`). Below we instantiate a small benchmark

    set.seed(4321)
    learner = lrn("classif.rpart", cp = 0.005)
    learner_rw = as_learner(po("reweighing_wts") %>>% learner)
    grd = benchmark_grid(list(task), list(learner, learner_rw), rsmp("cv", folds=3))
    bmr = benchmark(grd)
    #> INFO  [13:56:47.585] [mlr3] Running benchmark with 6 resampling iterations
    #> INFO  [13:56:47.625] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
    #> INFO  [13:56:47.692] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
    #> INFO  [13:56:47.746] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
    #> INFO  [13:56:47.798] [mlr3] Applying learner 'reweighing_wts.classif.rpart' on task 'adult_train' (iter 1/3)
    #> INFO  [13:56:47.897] [mlr3] Applying learner 'reweighing_wts.classif.rpart' on task 'adult_train' (iter 2/3)
    #> INFO  [13:56:48.005] [mlr3] Applying learner 'reweighing_wts.classif.rpart' on task 'adult_train' (iter 3/3)
    #> INFO  [13:56:48.102] [mlr3] Finished benchmark

We can now compute the metrics for our benchmark and see if reweighing actually improved fairness, measured via True Positive Rate (TPR) and classification accuracy (ACC):

    bmr$aggregate(msrs(c("fairness.tpr", "fairness.acc")))
    #>       nr     task_id                   learner_id resampling_id iters
    #>    <int>      <char>                       <char>        <char> <int>
    #> 1:     1 adult_train                classif.rpart            cv     3
    #> 2:     2 adult_train reweighing_wts.classif.rpart            cv     3
    #>    fairness.tpr fairness.acc
    #>           <num>        <num>
    #> 1:   0.07494903    0.1162688
    #> 2:   0.01151982    0.1054431
    #> Hidden columns: resample_result

    fairness_accuracy_tradeoff(bmr, msr("fairness.tpr"))

Our model became way fairer wrt. TPR but minimally worse wrt. accuracy!