为公平性算法提供标准化设计说明，方便对比与应用。
BiMi Sheets: Infosheets for bias mitigation methods
- 用统一模板记录偏见缓解方法的设计选择。
- 解决不同场景下方法不可移植的问题。
- 适合研究者和实践者快速评估与选用方案。
过去15年中，为实现机器学习中的公平性，已提出数百种偏见缓解方法。然而，算法偏见具有领域、任务和模型特异性，导致存在‘可迁移性陷阱’：某一情境下的缓解方案在另一情境中可能不适用。因此，设计偏见缓解方法时需考虑众多决策，如公平性的形式化定义，以及在机器学习流程中的干预位置与方式。这给方法的基准测试与比较带来挑战，并限制了实践者的采纳。本文提出 BiMi Sheets，作为通用、统一的指南，用于记录任何偏见缓解方法的设计选择，使研究者与实践者能快速了解其核心特性并进行匹配。此外，该结构化格式便于构建偏见缓解方法的数据库。为促进采用，我们提供了在线平台 bimisheet.com 以查找和创建 BiMi Sheets。
原文摘要 · Abstract (English)
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-, and model-specific, leading to a `portability trap': bias mitigation solutions in one context may not be appropriate in another. Thus, a myriad of design choices have to be made when creating a bias mitigation method, such as the formalization of fairness it pursues, and where and how it intervenes in the ML pipeline. This creates challenges in benchmarking and comparing the relative merits of different bias mitigation methods, and limits their uptake by practitioners. We propose BiMi Sheets as a portable, uniform guide to document the design choices of any bias mitigation method. This enables researchers and practitioners to quickly learn its main characteristics and to compare with their desiderata. Furthermore, the sheets' structure allow for the creation of a structured database of bias mitigation methods. In order to foster the sheets' adoption, we provide a platform for finding and creating BiMi Sheets at bimisheet.com.
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