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Unfair\nalgorithms can propagate such biases and produce predictions\nwith a disparate impact on various sensitive groups of\nindividuals (defined by sex, gender, ethnicity, religion,\nincome, socioeconomic status, physical or mental disabilities).\nFair algorithms possess the underlying foundation that these\ngroups should be treated similarly or have similar prediction\noutcomes. The fairness R package offers the calculation and\ncomparisons of commonly and less commonly used fairness metrics\nin population subgroups. These methods are described by Calders\nand Verwer (2010) , Chouldechova\n(2017) , Feldman et al. (2015)\n , Friedler et al. (2018)\n and Zafar et al. (2017)\n. 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