中文标题#
重新審視預處理組公平性：一個模組化基準測試框架
英文标题#
Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework
中文摘要#
隨著機器學習系統越來越多地融入高風險決策過程，確保算法結果的公平性已成為一個關鍵問題。 減輕偏差的方法通常分為三類：預處理、處理中和後處理。 儘管對後兩者給予了大量關注，但預處理方法在數據層面操作，具有模型無關性和提高隱私合規性等優勢，卻受到相對較少的關注，並且缺乏標準化的評估工具。 在本工作中，我們引入了 FairPrep，這是一個可擴展和模組化的基準框架，旨在對表格數據集上的公平意識預處理技術進行評估。 基於 AIF360 平台，FairPrep 允許數據集、公平干預和預測模型的無縫集成。 它提供了一個批處理接口，可以高效地進行實驗並自動生成公平性和效用指標的報告。 通過提供標準化的流程並支持可重複的評估，FairPrep 填補了公平性基準測試領域的關鍵空白，並為推進數據層面的公平性研究提供了實用的基礎。
英文摘要#
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.
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