标题： Fairmetrics：一个用于群体公平性评估的R包 显示英文标题

标题： Fairmetrics: An R package for group fairness evaluation

摘要： 公平性是机器学习（ML）的一个日益重要的领域，其重点是确保模型不会对特定群体产生系统性的偏差结果，尤其是那些由受保护属性（如种族、性别或年龄）定义的群体。 评估公平性是机器学习模型开发中的一个关键方面，因为有偏的模型可能会加剧结构性不平等。 {公平度量}R 包提供了一个用户友好的框架，用于严格评估多种基于群体的公平性标准，包括基于独立性（例如，统计公平性）、分离性（例如，等化几率）和充分性（例如，预测公平性）的指标。 基于群体的公平性标准评估模型在一组预定义群体中是否具有同等的准确性或校准度，以便可以实施适当的偏差缓解策略。 {公平度量}通过一个方便的包装函数提供了多个指标的点估计和区间估计，并包含了一个从重症监护医学信息库，第二版（MIMIC-II）数据库（Goldberger 等，2000；Raffa，2016）中得出的示例数据集。 显示英文摘要

摘要： 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).