一键评估模型对不同群体的公平性，防止算法歧视。
Fairmetrics: An R package for group fairness evaluation
- 提供多种公平性指标的统一计算接口
- 支持点估计与区间估计，结果更可靠
- 适合关注算法伦理的研究者和开发者
公平性是机器学习的重要方向，旨在确保模型不对特定群体（如种族、性别、年龄等受保护属性）产生系统性偏差。评估公平性是模型开发的关键环节，因有偏模型可能加剧结构性不平等。{fairmetrics} R 包为组公平性评估提供用户友好的框架，涵盖基于独立性（如统计均等）、分离性（如平等机会）和充分性（如预测均等）的多种公平性指标。该包通过便捷的封装函数提供多个指标的点估计与区间估计，并包含一个源自重症监护医疗信息库（MIMIC-II）数据库的示例数据集（Goldberger et al., 2000；Raffa, 2016），便于实际应用与验证。
原文摘要 · Abstract (English)
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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