fairness: fairness: Algorithmic Fairness Metrics

In kozodoi/Fairness: Algorithmic Fairness Metrics

fairness: Algorithmic Fairness Metrics

Description

The fairness package 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. The package also offers convenient visualizations to help understand fairness metrics.

Details

Package:	fairness
Depends:	R (>= 3.5.0)
Type:	Package
Version:	1.2.2
Date:	2021-04-14
License:	MIT
LazyLoad:	Yes

Author(s)

• Nikita Kozodoi [e-mail]

• Tibor V. Varga [e-mail]

See Also

https://github.com/kozodoi/fairness https://kozodoi.me/r/fairness/packages/2020/05/01/fairness-tutorial.html

kozodoi/Fairness documentation built on March 22, 2023, 12:29 p.m.