mlr3fairness: Fairness Auditing and Debiasing for 'mlr3'

Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem. This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as "Reweighing" described in 'Kamiran, Calders' (2012) <doi:10.1007/s10115-011-0463-8> and "Equalized Odds" described in 'Hardt et al.' (2016) <>. Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods.

Version: 0.3.0
Depends: R (≥ 3.5.0), mlr3 (≥ 0.13.0)
Imports: checkmate, R6 (≥ 2.4.1), data.table (≥ 1.13.6), paradox, mlr3measures, mlr3misc, mlr3pipelines, ggplot2
Suggests: mlr3viz, rmarkdown, knitr, rpart, testthat (≥ 3.0.0), patchwork, ranger, mlr3learners, linprog, posterdown, kableExtra, fairml, iml
Published: 2022-05-12
Author: Florian Pfisterer ORCID iD [cre, aut], Wei Siyi [aut], Michel Lang ORCID iD [aut]
Maintainer: Florian Pfisterer <pfistererf at>
License: LGPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mlr3fairness results


Reference manual: mlr3fairness.pdf
Vignettes: Debiasing Methods
Fairness Metrics
Fairness Visualizations


Package source: mlr3fairness_0.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlr3fairness_0.3.0.tgz, r-oldrel (arm64): mlr3fairness_0.3.0.tgz, r-release (x86_64): mlr3fairness_0.3.0.tgz, r-oldrel (x86_64): mlr3fairness_0.3.0.tgz


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