fairness

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) doi:10.1007/s10618-010-0190-x, Chouldechova (2017) doi:10.1089/big.2016.0047, Feldman et al. (2015) doi:10.1145/2783258.2783311 , Friedler et al. (2018) doi:10.1145/3287560.3287589 and Zafar et al. (2017) doi:10.1145/3038912.3052660. The package also offers convenient visualizations to help understand fairness metrics.

Last 30 days

This package has been downloaded 626 times in the last 30 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 24 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 4,516 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Mar 24, 2025 with 66 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.