用机器学习识别英语数据中的偏见，揭示其局限与复杂性。
Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
- 构建模型识别语言偏见，关注数据本身而非强行消除
- 实证发现偏见具有语境依赖性，难以完全消除
- 适合信息与文化遗产从业者参考其应用边界
尽管已有大量努力试图缓解机器学习系统的偏见，但这些系统仍持续伤害已处于边缘地位的人群。主流机器学习方法通常假设偏见可被清除、公平模型可被构建，但我们表明这并非总是可能或可取的目标。本文通过创建模型来识别语言偏见，将关注点从消除偏见转向揭示数据中的偏见。通过一次工作坊，我们评估了该模型在信息与文化遗产专业人士工作流中的具体应用场景。研究结果表明，由于偏见的语境特性，机器学习在识别偏见方面存在局限，且缓解偏见的方法可能同时赋予某些群体优势而压迫其他群体，偏见具有不可避免性。本文强调需扩展机器学习在偏见与公平问题上的方法论，提出一种混合方法学框架，以评估特定使用场景下消除偏见或实现公平的可行性。
原文摘要 · Abstract (English)
Despite numerous efforts to mitigate their biases, ML systems continue to harm already-marginalized people. While predominant ML approaches assume bias can be removed and fair models can be created, we show that these are not always possible, nor desirable, goals. We reframe the problem of ML bias by creating models to identify biased language, drawing attention to a dataset's biases rather than trying to remove them. Then, through a workshop, we evaluated the models for a specific use case: workflows of information and heritage professionals. Our findings demonstrate the limitations of ML for identifying bias due to its contextual nature, the way in which approaches to mitigating it can simultaneously privilege and oppress different communities, and its inevitability. We demonstrate the need to expand ML approaches to bias and fairness, providing a mixed-methods approach to investigating the feasibility of removing bias or achieving fairness in a given ML use case.
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