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fairness  

Algorithmic Fairness Metrics
View on CRAN: Click here


Download and install fairness package within the R console
Install from CRAN:
install.packages("fairness")

Install from Github:
library("remotes")
install_github("cran/fairness")

Install by package version:
library("remotes")
install_version("fairness", "1.2.2")



Attach the package and use:
library("fairness")
Maintained by
Nikita Kozodoi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-09-27
Latest Update: 2021-04-14
Description:
Offers calculation, visualization and comparison of algorithmic fairness metrics. Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) , Chouldechova (2017) , Feldman et al. (2015) , Friedler et al. (2018) and Zafar et al. (2017) . The package also offers convenient visualizations to help understand fairness metrics.
How to cite:
Nikita Kozodoi (2019). fairness: Algorithmic Fairness Metrics. R package version 1.2.2, https://cran.r-project.org/web/packages/fairness. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.1 (2019-09-27 11:00), 1.1.0 (2020-05-02 00:30), 1.1.1 (2020-07-26 21:00), 1.2.0 (2020-11-19 15:10), 1.2.1 (2021-03-31 11:10)
Other packages that cited fairness R package
View fairness citation profile
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Complete documentation for fairness
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