Machine Learning Fairness Extension for mlr3.
Install the development version from github:
Machine Learning model predictions can be skewed by a range of factors and thus might be considered unfair towards certain groups or individuals. An example would be the COMPAS algorithm, which is a popular commercial algorithm used by judges and parole officers for scoring criminal defendant’s likelihood of reoffending (recidivism). Studies have shown, that the algorithm might be biased in favor of white defendants. Biases can occur in a large variety of situations where algorithms automate or support human decision making e.g. credit checks, automatic HR tools along with a variety of other domains.
The goal of
mlr3fairness is to allow for auditing of
mlr3 learners, visualization and subsequently trying to improve fairness using debiasing strategies.
⚠️ Note Bias auditing and debiasing solely based on observational data can not guarantee fairness of a decision making system. Several biases, for example comming from the data can not be detected using the approaches implemented in
mlr3fairness. The goal of this software is instead to allow for a better understanding and first hints at possible fairness problems in a studied model.
Fairness Measures: Audit algorithmms for fairness using a variety of fairness criteria. This also allows for designing custom criteria.
Fairness Visualizations: Diagnose fairness problems through visualizations.
Debiasing Methods: Correct fairness problems in three lines of code.
Fairness Report: Obtain a report regarding an algorithm’s fairness. (Under development)
mlr3fairness requires information about the protected attribute wrt. which we want to assess fairness. This can be set via the
col_role “pta” (protected attribute).
task$col_roles$pta = "variable_name"
In case a non-categorical or more complex protected attribute is required, it can be manually computed and added to the task.
mlr3fairness does not require specific types for
pta, but will compute one metric for every unique value in the
mlr3fairness offers a variety of fairness metrics. Metrics are prefixed with
fairness. and can be found in the
msr() dictionary. Most fairness metrics are based on a difference between two protected groups (e.g. male and female) for a given metric (e.g. the false positive rate:
fpr). See the vignette for a more in-depth introduction to fairness metrics and how to choose them.
|fairness.acc||Absolute differences in accuracy across groups|
|fairness.mse||Absolute differences in mean squared error across groups|
|fairness.fnr||Absolute differences in false negative rates across groups|
|fairness.fpr||Absolute differences in false positive rates across groups|
|fairness.tnr||Absolute differences in true negative rates across groups|
|fairness.tpr||Absolute differences in true positive rates across groups|
|fairness.npv||Absolute differences in negative predictive values across groups|
|fairness.ppv||Absolute differences in positive predictive values across groups|
|fairness.fomr||Absolute differences in false omission rates across groups|
|fairness.fp||Absolute differences in false positives across groups|
|fairness.tp||Absolute differences in true positives across groups|
|fairness.tn||Absolute differences in true negatives across groups|
|fairness.fn||Absolute differences in false negatives across groups|
|fairness.cv||Difference in positive class prediction, also known as Calders-Wevers gap or demographic parity|
|fairness.eod||Equalized Odds: Mean of absolute differences between true positive and false positive rates across groups|
|fairness.pp||Predictive Parity: Mean of absolute differences between ppv and npv across groups|
|fairness.acc_eod=.05||Accuracy under equalized odds < 0.05 constraint|
|fairness.acc_ppv=.05||Accuracy under ppv difference < 0.05 constraint|
Additional custom fairness metrics can be easily constructed, the vignette contains more details. The
fairness_tensor() function can be used with a
Prediction in order to print group-wise confusion matrices for each protected attribute group. We can furthermore measure fairrness in each group separately using
Visualizations can be used with either a
ResampleResult or a
BenchmarkResult. For more information regarding those objects, refer to the mlr3 book.
fairness_accuracy_tradeoff: Plot available trade-offs between fairness and model performance.
compare_metrics: Compare fairness across models and cross-validation folds.
fairness_prediction_density: Density plots for each protected attribute.
Debiasing methods can be used to improve the fairness of a given model.
mlr3fairness includes several methods that can be used together with
mlr3pipelines to obtain fair(er) models:
library(mlr3pipelines) lrn = as_learner(po("reweighing_wts") %>>% lrn("classif.rpart")) rs = resample(lrn, task = tsk("compas")$filter(1:500), rsmp("cv")) rs$score(msr("fairness.acc"))
mlr3fairness furthermore contains several learners that can be used to directly learn fair models:
|regr.fairfrrm||fairml||Scutari et al., 2021|
|classif.fairfgrrm||fairml||Scutari et al., 2021|
|regr.fairzlm||fairml||Zafar et al., 2019|
|classif.fairzlrm||fairml||Zafar et al., 2019|
|regr.fairnclm||fairml||Komiyama et al., 2018|
You can load them using
An important step towards achieving more equitable outcomes for ML models is adequate documentation for datasets and models in machine learning.
mlr3fairness comes with reporting aides for
datasets. This provides empty templates that can be used to create interactive reports through
||Modelcard for ML models||Mitchell et al., 2018||link|
||Datasheet for data sets||Gebru et al., 2018||link|
report_* functions instantiate a new
.Rmd template that contains a set of pre-defined questions which can be used for reporting as well as initial graphics. The goal is that a user extends this
.Rmd file to create comprehensive documentation for datasets, ML models or to document a model’s fairness. It can later be converted into a
html report using
We provide a short example detailing how
mlr3fairness integrates with the
library(mlr3fairness) #Initialize Fairness Measure fairness_measure = msr("fairness.fpr") #Initialize tasks task_train = tsk("adult_train") task_test = tsk("adult_test") #Initialize model learner = lrn("classif.rpart", predict_type = "prob") #Verify fairness metrics learner$train(task_train) predictions = learner$predict(task_test) predictions$score(fairness_measure, task = task_test) #Visualize the predicted probability score based on protected attribute. fairness_prediction_density(predictions, task_test)
- The mcboost package integrates with mlr3 and offers additional debiasing post-processing functionality for classification, regression and survival.
- The AI Fairness 360 toolkit offers an R extension that allows for bias auditing, visualization and mitigation.
- fairmodels integrates with the DALEX R-packages and similarly allows for bias auditing, visualization and mitigation.
- The fairness package allows for bias auditing in R.
- The fairml package contains methods for learning de-biased regression and classification models. Learners from
fairmlare included as learners in
- Aequitas Allows for constructing a fairness report for different fairness metrics along with visualization in Python.
- fairlearn Allows for model auditing and debiasing as well as visualization in Python.
- AI Fairness 360 Allows for model auditing and debiasing as well as visualization in R and Python.
mlr3fairness is a free and open source software project that encourages participation and feedback. If you have any issues, questions, suggestions or feedback, please do not hesitate to open an “issue” about it on the GitHub page! In case of problems / bugs, it is often helpful if you provide a “minimum working example” that showcases the behaviour.