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 that uses ridge regression to enforce fairness.

Flexible Tool for Bias Detection, Visualization, and Mitigation
c(person("Jakub", "Wiśniewski", role = c("aut", "cre"),
email = "jakwisn@gmail.com"),
person("Przemysław", "Biecek", role = c("aut"),
comment = c(ORCID = "0000-0001-8423-1823")))
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 preprocessing and post-processing bias mitigation algorithms implemented.
DALEX, ggplot2, patchwork, ggdendro, ggrepel, scales
ranger, gbm, knitr, rmarkdown, covr, testthat, spelling
Jakub Wiśniewski [aut, cre],
Przemysław Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>)

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.

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>.