fairml: Fair Models in Machine Learning

Fair machine learning regression models which take sensitive attributes into account in model estimation. Currently implementing Komiyama et al. (2018) <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>, Zafar et al. (2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own approach from Scutari, Panero and Proissl (2022) <https://link.springer.com/content/pdf/10.1007/s11222-022-10143-w.pdf> that uses ridge regression to enforce fairness.

Version: 0.8
Depends: R (≥ 3.5.0)
Imports: methods, glmnet
Suggests: lattice, gridExtra, parallel, cccp, CVXR, survival
Published: 2023-05-13
DOI: 10.32614/CRAN.package.fairml
Author: Marco Scutari [aut, cre]
Maintainer: Marco Scutari <scutari at bnlearn.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: fairml results


Reference manual: fairml.pdf


Package source: fairml_0.8.tar.gz
Windows binaries: r-devel: fairml_0.8.zip, r-release: fairml_0.8.zip, r-oldrel: fairml_0.8.zip
macOS binaries: r-release (arm64): fairml_0.8.tgz, r-oldrel (arm64): fairml_0.8.tgz, r-release (x86_64): fairml_0.8.tgz, r-oldrel (x86_64): fairml_0.8.tgz
Old sources: fairml archive

Reverse dependencies:

Reverse suggests: mlr3fairness


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