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> and an improvement over the former that uses ridge regression to enforce fairness.

Version: 0.4
Depends: R (≥ 3.5.0)
Imports: methods, optiSolve, glmnet
Suggests: lattice
Published: 2021-04-15
Author: Marco Scutari [aut, cre]
Maintainer: Marco Scutari <marco.scutari at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: fairml results

Downloads:

Reference manual: fairml.pdf
Package source: fairml_0.4.tar.gz
Windows binaries: r-devel: fairml_0.4.zip, r-release: fairml_0.4.zip, r-oldrel: fairml_0.4.zip
macOS binaries: r-release: fairml_0.4.tgz, r-oldrel: fairml_0.4.tgz
Old sources: fairml archive

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