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, but given there are no biases in the labelling process one other option is to debias models using either preprocessing, postprocessing and inprocessing methods. mlr3fairness provides some operators as PipeOps for mlr3pipelines. If you are not familiar with mlr3pipelines, the mlr3 book

We again showcase debiasing using the adult_train task:

#> Loading required package: mlr3

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 input.type.train input.type.predict output.type.train output.type.predict
EOd TaskClassif TaskClassif NULL PredictionClassif
reweighing_os TaskClassif TaskClassif TaskClassif TaskClassif
reweighing_wts TaskClassif 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

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  [18:31:37.935] [mlr3] Running benchmark with 6 resampling iterations 
#> INFO  [18:31:38.051] [mlr3] Applying learner 'reweighing_wts.classif.rpart' on task 'adult_train' (iter 1/3) 
#> INFO  [18:31:38.456] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3) 
#> INFO  [18:31:38.635] [mlr3] Applying learner 'reweighing_wts.classif.rpart' on task 'adult_train' (iter 3/3) 
#> INFO  [18:31:38.923] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3) 
#> INFO  [18:31:39.093] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3) 
#> INFO  [18:31:39.281] [mlr3] Applying learner 'reweighing_wts.classif.rpart' on task 'adult_train' (iter 2/3) 
#> INFO  [18:31:39.573] [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      resample_result     task_id                   learner_id
#> 1:  1 <ResampleResult[22]> adult_train                classif.rpart
#> 2:  2 <ResampleResult[22]> adult_train reweighing_wts.classif.rpart
#>    resampling_id iters fairness.tpr fairness.acc
#> 1:            cv     3   0.07494903    0.1162688
#> 2:            cv     3   0.01151982    0.1054431
fairness_accuracy_tradeoff(bmr, msr("fairness.tpr"))

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