fairmetrics: An R package for group fairness evaluation

• URL: http://arxiv.org/abs/2506.06243v2
• Date: Thu, 19 Jun 2025 00:00:46 GMT
• Title: fairmetrics: An R package for group fairness evaluation
• Authors: Benjamin Smith, Jianhui Gao, Jessica Gronsbell,
• Abstract summary: fairmetrics R package offers a user-friendly framework for rigorously evaluating group-based fairness criteria.<n>Group-based fairness criteria assess whether a model is equally accurate or well-calibrated across a set of predefined groups.<n>fairmetrics provides both point and interval estimates for multiple metrics through a convenient wrapper function.
• Score: 0.40964539027092906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness is a growing area of machine learning (ML) that focuses on ensuring models do not produce systematically biased outcomes for specific groups, particularly those defined by protected attributes such as race, gender, or age. Evaluating fairness is a critical aspect of ML model development, as biased models can perpetuate structural inequalities. The {fairmetrics} R package offers a user-friendly framework for rigorously evaluating numerous group-based fairness criteria, including metrics based on independence (e.g., statistical parity), separation (e.g., equalized odds), and sufficiency (e.g., predictive parity). Group-based fairness criteria assess whether a model is equally accurate or well-calibrated across a set of predefined groups so that appropriate bias mitigation strategies can be implemented. {fairmetrics} provides both point and interval estimates for multiple metrics through a convenient wrapper function and includes an example dataset derived from the Medical Information Mart for Intensive Care, version II (MIMIC-II) database (Goldberger et al., 2000; Raffa, 2016).

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