标题： 重新审视预处理组公平性：一个模块化基准测试框架 显示英文标题

标题： Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework

摘要： 随着机器学习系统越来越多地融入高风险决策过程，确保算法结果的公平性已成为一个关键问题。 减轻偏差的方法通常分为三类：预处理、处理中和后处理。 尽管对后两者给予了大量关注，但预处理方法在数据层面操作，具有模型无关性和提高隐私合规性等优势，却受到相对较少的关注，并且缺乏标准化的评估工具。 在本工作中，我们引入了 FairPrep，这是一个可扩展和模块化的基准框架，旨在对表格数据集上的公平意识预处理技术进行评估。 基于 AIF360 平台，FairPrep 允许数据集、公平干预和预测模型的无缝集成。 它提供了一个批处理接口，可以高效地进行实验并自动生成公平性和效用指标的报告。 通过提供标准化的流程并支持可重复的评估，FairPrep 填补了公平性基准测试领域的关键空白，并为推进数据层面的公平性研究提供了实用的基础。 显示英文摘要

摘要： As machine learning systems become increasingly integrated into high-stakes decision-making processes, ensuring fairness in algorithmic outcomes has become a critical concern. Methods to mitigate bias typically fall into three categories: pre-processing, in-processing, and post-processing. While significant attention has been devoted to the latter two, pre-processing methods, which operate at the data level and offer advantages such as model-agnosticism and improved privacy compliance, have received comparatively less focus and lack standardised evaluation tools. In this work, we introduce FairPrep, an extensible and modular benchmarking framework designed to evaluate fairness-aware pre-processing techniques on tabular datasets. Built on the AIF360 platform, FairPrep allows seamless integration of datasets, fairness interventions, and predictive models. It features a batch-processing interface that enables efficient experimentation and automatic reporting of fairness and utility metrics. By offering standardised pipelines and supporting reproducible evaluations, FairPrep fills a critical gap in the fairness benchmarking landscape and provides a practical foundation for advancing data-level fairness research.