Description: Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented.

Description: Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.

Description: Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.

Imports: DALEX, ggplot2, scales, stats, patchwork,
Suggests: ranger, gbm, knitr, rmarkdown, covr, testthat, spelling,

Authors@R: c(person("Nikita", "Kozodoi", email = "nikita.kozodoi@hu-berlin.de", role = c("aut", "cre")),
person("Tibor", "V. Varga", email = "tirgit@hotmail.com", role = c("aut"), comment = c(ORCID = "0000-0002-2383-699X")))
Description: Offers various metrics of algorithmic fairness. Fairness in machine learning is an emerging
topic with the overarching aim to critically assess algorithms (predictive and classification models) whether
their results reinforce existing social biases. While unfair algorithms can propagate such biases and offer
prediction or classification results with a disparate impact on various sensitive subgroups of populations (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 / should have similar 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) <doi:10.1007/s10618-010-0190-x>, Chouldechova
offers convenient visualizations to help understand fairness metrics.
Imports: caret, devtools, e1071, ggplot2, pROC