mlr3fairness

mlr3fairness-package

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) tools:::Rd_expr_doi("10.1007/s10115-011-0463-8") and "Equalized Odds" described in 'Hardt et al.' (2016) <a href="https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf">https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf</a>. Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods.

internal

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)
<https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
Integration with 'mlr3' allows for auditing of ML models as well as
convenient joint tuning of machine learning algorithms and debiasing
methods.

Florian Pfisterer

Fairness Auditing and Debiasing for 'mlr3'

Wei Siyi

Michel Lang

mlr3fairness-package function

Maintainer: Florian Pfisterer <a href="/link/pfistererf%40googlemail.com?package=mlr3fairness&version=0.4.0" data-mini-rdoc="mlr3fairness::pfistererf@googlemail.com">pfistererf@googlemail.com</a> (<a href="https://orcid.org/0000-0001-8867-762X">ORCID</a>)
Authors:<ul>
<li>Wei Siyi <a href="/link/weisiyi2%40gmail.com?package=mlr3fairness&version=0.4.0" data-mini-rdoc="mlr3fairness::weisiyi2@gmail.com">weisiyi2@gmail.com</a></li>
<li>Michel Lang <a href="/link/michellang%40gmail.com?package=mlr3fairness&version=0.4.0" data-mini-rdoc="mlr3fairness::michellang@gmail.com">michellang@gmail.com</a> (<a href="https://orcid.org/0000-0001-9754-0393">ORCID</a>)</li>
</ul>

Author

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) tools:::Rd_expr_doi("10.1007/s10115-011-0463-8") and "Equalized Odds" described in 'Hardt et al.' (2016) <a href='https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf'>https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf</a>. Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods.

mlr3fairness: Fairness Auditing and Debiasing for 'mlr3' — mlr3fairness-package

Maintainer: Florian Pfisterer <a href='mailto:pfistererf@googlemail.com'>pfistererf@googlemail.com</a> (<a href='https://orcid.org/0000-0001-8867-762X'>ORCID</a>)
Authors:<ul>
<li>Wei Siyi <a href='mailto:weisiyi2@gmail.com'>weisiyi2@gmail.com</a></li>
<li>Michel Lang <a href='mailto:michellang@gmail.com'>michellang@gmail.com</a> (<a href='https://orcid.org/0000-0001-9754-0393'>ORCID</a>)</li>
</ul>

mlr3fairness-package: mlr3fairness: Fairness Auditing and Debiasing for 'mlr3'

Description

Arguments

Author

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